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Journal articles on the topic 'Topic evolution networks'
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Liu, Yanni, Dongsheng Liu, and Yuwei Chen. "Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City." Complexity 2020 (June 4, 2020): 1–13. http://dx.doi.org/10.1155/2020/9789431.
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With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.
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Wang, Xiang Yang. "Hot Topic Detection in News Blog." Applied Mechanics and Materials 513-517 (February 2014): 1114–18. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1114.
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Topic detection in news blogs needs to carefully arrange the information and analyze the characteristics of topics. However, there are some difficulties for hot topic detection in blogs. On one hand, information overload and dynamic change of web pages are obstacles of information arrangement. On the other hand, there are different hotness evaluation norms for web topics. The proposed method first analyzes the characteristics of the news blog and recognizes the factors which can influence the evolution of a topic. Then a word network is constructed, and candidate topics are extracted from the word network based on the complex networks theory. Finally, hot topics in the news blog are identified by measuring the user participation, opinion communication between users and user forgetting degree.
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Jung, Sukhwan, and Aviv Segev. "Analyzing the generalizability of the network-based topic emergence identification method." Semantic Web 13, no. 3 (April 6, 2022): 423–39. http://dx.doi.org/10.3233/sw-212951.
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Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.
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Xu, Xiaoyan, Wei Lv, Beibei Zhang, Shuaipeng Zhou, Wei Wei, and Yusen Li. "A Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social Networks." Complexity 2021 (August 26, 2021): 1–14. http://dx.doi.org/10.1155/2021/8859225.
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With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is composed of three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks.
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Liang, Wei, Zixian Lu, Qun Jin, Yonghua Xiong, and Min Wu. "Modeling and Analyzing of Research Topic Evolution Associated with Social Networks of Researchers." International Journal of Distributed Systems and Technologies 7, no. 3 (July 2016): 42–62. http://dx.doi.org/10.4018/ijdst.2016070103.
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Research trends keep evolving along the time with certain trackable patterns. Mining academic literature and discovering the latent research trends evolution is an interesting and important problem. Few of previous studies focusing on academic topic evolution modeling have addressed the temporal topic evolution patterns. In addition, researchers' profile and their social networks are valuable complementary to the research trends tracking. In this study, to analyze the underlying research trends evolution along with the scientific collaborations of researchers, a novel temporal research trends evolution model associated with researchers' social networks is proposed and built. Specifically, the detected research topics are classified into different clusters in each timeslot, and the evolution patterns are deduced among these topic clusters. The effectiveness of our approach is evaluated based on a real academic dataset. The experimental results can help users to discover the major research trends for specific fields. Besides, the tracked statuses of the corresponding scientific groups are helpful for searching research trends or finding collaboration opportunities according to researchers' different requirements.
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Jensen, Scott, Xiaozhong Liu, Yingying Yu, and Staša Milojevic. "Generation of topic evolution trees from heterogeneous bibliographic networks." Journal of Informetrics 10, no. 2 (May 2016): 606–21. http://dx.doi.org/10.1016/j.joi.2016.04.002.
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Brunilde Sanso, Lorela Cano, and Antonio Capone. "On the evolution of infrastructure sharing in mobile networks: A survey." ITU Journal on Future and Evolving Technologies 1, no. 1 (December 21, 2020): 141–57. http://dx.doi.org/10.52953/nbqh9604.
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Infrastructure sharing for mobile networks has been a prolific research topic for more than three decades now. The key driver for Mobile Network Operators to share their network infrastructure is cost reduction. Spectrum sharing is often studied alongside infrastructure sharing although on its own it is a vast research topic outside the scope of this survey. Instead, in this survey we aim to provide a complete picture of infrastructure sharing both over time and in terms of research branches that have stemmed from it such as performance evaluation, resource management etc. We also put an emphasis on the relation between infrastructure sharing and the decoupling of infrastructure from services, wireless network virtualization and multi-tenancy in 5G networks. Such a relation reflects the evolution of infrastructure sharing over time and how it has become a commercial reality in the context of 5G.
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Lee, Won Sang. "Analyzing the Evolution of Interdisciplinary Areas." Journal of Global Information Management 30, no. 1 (January 1, 2022): 1–23. http://dx.doi.org/10.4018/jgim.304062.
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Recently, various new areas of research have been of great interest to researchers. As these areas are highly based on academic and industrial needs, it is necessary to examine the change and evolution in research. This study proposed a framework for identifying emerging areas and their evolution. The proposed framework suggests that latent Dirichlet allocation is applied to identify emerging topics and their networks in such interdisciplinary areas. The simulation for empirical network analysis was then applied to the identified topic networks to terminate continuous evolution. The proposed framework is applied to a smart city, which is one of the most interdisciplinary and fast-evolving areas. These findings indicate that the evolution of smart transportation and smart grids is likely to be the focus. The findings also indicate that newly emerging research may lack openness and diversity. This study contributes to further investigate research trends and planning research strategies for new and interdisciplinary areas.
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Cai, Meng, Han Luo, and Ying Cui. "A Study on the Topic-Sentiment Evolution and Diffusion in Time Series of Public Opinion Derived from Emergencies." Complexity 2021 (December 2, 2021): 1–23. http://dx.doi.org/10.1155/2021/2069010.
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With the development of the Internet, social media has become an important platform for people to deal with emergencies and share information. When a public health emergency occurs, the public can understand the topics of the event and perceive the sentiments of others through social media, thus building a cooperative communication network. In this study, we took the public health emergency as the main research object and the natural disaster, accident, and social security event as the secondary research object and further revealed the law of the formation and evolution of public opinion through the analysis on temporal networks of topics and sentiments in social media platforms. Firstly, we identified the derived topics by constructing the topic model and used the sentiment classification model to divide the text sentiments of the derived topics into two types: positive sentiment and negative sentiment. Then, the ARIMA time series model was used to fit and predict the evolution and diffusion rules of topics and sentiments derived from public opinions on temporal networks. It was found that the evolution law of derived public opinions had similarities and differences in various types of emergencies and was closely related to government measures and media reports. The related research provides a foundation for the management of network public opinion and the realization of better emergency effects.
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Gomez, Manuel J., José A. Ruipérez-Valiente, and Félix J. García Clemente. "Exploring Technology- and Sensor-Driven Trends in Education: A Natural-Language-Processing-Enhanced Bibliometrics Study." Sensors 23, no. 23 (November 21, 2023): 9303. http://dx.doi.org/10.3390/s23239303.
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Over the last decade, there has been a large amount of research on technology-enhanced learning (TEL), including the exploration of sensor-based technologies. This research area has seen significant contributions from various conferences, including the European Conference on Technology-Enhanced Learning (EC-TEL). In this research, we present a comprehensive analysis that aims to identify and understand the evolving topics in the TEL area and their implications in defining the future of education. To achieve this, we use a novel methodology that combines a text-analytics-driven topic analysis and a social network analysis following an open science approach. We collected a comprehensive corpus of 477 papers from the last decade of the EC-TEL conference (including full and short papers), parsed them automatically, and used the extracted text to find the main topics and collaborative networks across papers. Our analysis focused on the following three main objectives: (1) Discovering the main topics of the conference based on paper keywords and topic modeling using the full text of the manuscripts. (2) Discovering the evolution of said topics over the last ten years of the conference. (3) Discovering how papers and authors from the conference have interacted over the years from a network perspective. Specifically, we used Python and PdfToText library to parse and extract the text and author keywords from the corpus. Moreover, we employed Gensim library Latent Dirichlet Allocation (LDA) topic modeling to discover the primary topics from the last decade. Finally, Gephi and Networkx libraries were used to create co-authorship and citation networks. Our findings provide valuable insights into the latest trends and developments in educational technology, underlining the critical role of sensor-driven technologies in leading innovation and shaping the future of this area.
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Ali, Faizan, Eunhye (Olivia) Park, Junehee Kwon, and Bongsug (Kevin) Chae. "30 years of contemporary hospitality management." International Journal of Contemporary Hospitality Management 31, no. 7 (July 8, 2019): 2641–65. http://dx.doi.org/10.1108/ijchm-10-2018-0832.
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Purpose This paper aims to showcase the trends in the research topics and their contributors over a time period of 30 years in the International Journal of Contemporary Hospitality Management (IJCHM). To be specific, this paper uncovers IJCHM’s latent topics and hidden patterns in published research and highlights the differences across three decades and before and after Social Sciences Citation indexing. Design/methodology/approach In total, 1,573 documents published over 199 issues of IJCHM were analyzed using two computational tools, i.e. metaknowledge and structural topic modeling (STM), as the basis of the mixed method. STM was used to discover the evolution of topics over time. Moreover, bibliometrics (and network analysis) were used to highlight IJCHM’s top researchers, top-cited references, the geographical networks of the researchers and differences in the collaborative networks. Findings The number of papers published continually increased over time with changes of key researchers publishing in IJCHM. The co-authorship networks have also changed and revealed an increasing diversity of authorship and collaborations among authors in different countries. Moreover, the variety of topics and the relative weight of each topic have also changed. Research limitations/implications Based on the findings of this study, theoretical and practical implications for hospitality and tourism researchers are provided. Originality/value It is the first attempt to apply topic modeling to a leading academic journal in hospitality and tourism and explore the diversity in contemporary hospitality management research (topics and contributors) from 30 years of published research.
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YANG, HAN-XIN, WEN-XU WANG, YING-CHENG LAI, and CELSO GREBOGI. "THE EMERGENCE AND EVOLUTION OF COOPERATION ON COMPLEX NETWORKS." International Journal of Bifurcation and Chaos 22, no. 09 (September 2012): 1250228. http://dx.doi.org/10.1142/s0218127412502288.
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The emergence and evolution of cooperation in complex natural, social and economical systems is an interdisciplinary topic of recent interest. This paper focuses on the cooperation on complex networks using the approach of evolutionary games. In particular, the phenomenon of diversity-optimized cooperation is briefly reviewed and the effect of network clustering on cooperation is treated in detail. For the latter, a general type of public goods games is used with the result that, for fixed average degree and degree distributions in the underlying network, a high clustering coefficient can promote cooperation. Basic quantities such as the cooperator and defector clusters, mean payoffs of cooperators and defectors along their respective boundaries, the fraction of cooperators for different classes as well as the mean payoffs of hubs in scale-free networks are also investigated. Since strong clustering is typical in many social networks, these results provide insights into the emergence of cooperation in such networks.
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Chen, Mo. "Research on Key Technology of Web Hierarchical Topic Detection and Evolution Based on Behaviour Tracking Analysis." International Journal of Computers Communications & Control 14, no. 3 (May 31, 2019): 311–28. http://dx.doi.org/10.15837/ijccc.2019.3.3534.
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In the development background of today’s big data era, the research direction of Web hierarchical topic detection and evolution characterized by the semistructured or unstructured data has caught wide attention for academicians. This paper proposes an idea of Web hierarchical topic detection and evolution based on behaviour tracking analysis taking the network big data as the research object, and expounds main implementation methods, which include the instance analysis of the usage mode, the instance analysis of the seed, the set analysis of similar instance supporting the topics, the set analysis of similar instance supporting the events, the evolution analysis of the event, and expounds the algorithm of Web hierarchical topic detection and evolution based on behaviour tracking analysis. The process of experimental analysis is organized as follows, first of all, the experiment analyses the quality of topic detection, the accuracy rate with the number of instance concerned and the seed threshold variation trend, the accuracy rate with the number of instance concerned and the probability threshold variation trend, secondly, the experiment analyses the quality of topic evolution, the accuracy rate with the variation trend of parameter adjustment, the accuracy rate with the number of instance concerned and the similar threshold variation trend, finally, the experiment analyses the time consuming to solve main research problem under different method, the qualitative result of topic detection and evolution under different data set. The results of experimental analysis show the idea is feasible, verifiable and superior, which plays a major role in reconfiguring Web hierarchical topic corpus and providing an intelligent big data warehouse for the network information evolution application.
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Wang, Jiwu, and Hongbo Sun. "An evolution simulation framework for ecological structure of crowd networks." International Journal of Crowd Science 4, no. 1 (December 16, 2019): 87–100. http://dx.doi.org/10.1108/ijcs-09-2019-0022.
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Purpose This paper aims to obtain optimal specialization mode and level for complex network or system structures. In the e-commerce system, this paper studies the changes of each transaction subject in the process of ecological structure based on the income level of each transaction subject. Design/methodology/approach This paper aims to research the change of transaction efficiency evolution process of intermediaries. With the improvement of transaction efficiency, intermediaries interact with other transaction subjects at given modes in e-commerce systems. This paper analyzes the relationship between the factors of production and trade and explains the quantitative relationship between them in the form of mathematical modeling. An evolution simulation framework is established to elaborate the simulation process and method of crowd network in e-commerce ecosystem and then sets up the simulation experiment. Findings During simulation processes, the changes of data are observed and analyzed to obtain the optimal evolution paths and specialization modes. Furthermore, this paper provides solid supports for the research of the quantitative analysis of ecological structure evolutions. Originality/value Evolution simulation of ecological structure is first proposed in the topic of crowd network. It is with the aid of the concept of ecology, the theory and method, simulation of complex network structure and system structure. This paper analyses and researches the evolution process of optimal specialization modes and intelligent level of crowd networks with transaction efficiency changing. The ecological structure optimal evolution paths can be obtained by trend of simulations.
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Yu, Tianyuan, Liang Bai, Jinlin Guo, and Zheng Yang. "Construct a Bipartite Signed Network in YouTube." International Journal of Multimedia Data Engineering and Management 6, no. 4 (October 2015): 56–77. http://dx.doi.org/10.4018/ijmdem.2015100104.
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Nowadays, the video-sharing websites are becoming more and more popular, which leads to latent social networks among videos and users. In this work, results are integrated with the data collected from YouTube, one of the largest user-driven online video repositories, and are supported by Chinese sentiment analysis which excels the state of art. Along with it, the authors construct two types of bipartite signed networks, video network (VN) and topic participant network (TPN), where nodes denote videos or users while weights of edges represent the correlation between the nodes. Several indices are defined to quantitatively evaluate the importance of the nodes in the networks. Experiments are conducted by using YouTube videos and corresponding metadata related to two specific events. Experimental results show that both the analysis of social networks and indices correspond very closely with the events' evolution and the roles that topic participants play in spreading Internet videos. Finally, the authors extend the networks to summarization of a video set related to an event.
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Chen, Huarong, Yadong Wu, Huaquan Tang, Jing Lei, Guijuan Wang, Weixin Zhao, Jing Liao, Fupan Wang, and Zhong Wang. "Visual Analysis Method for Traffic Trajectory with Dynamic Topic Movement Patterns Based on the Improved Markov Decision Process." Electronics 13, no. 3 (January 23, 2024): 467. http://dx.doi.org/10.3390/electronics13030467.
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The visual analysis of trajectory topics is helpful for mining potential trajectory patterns, but the traditional visual analysis method ignores the evolution of the temporal coherence of the topic. In this paper, a novel visual analysis method for dynamic topic analysis of traffic trajectory is proposed, which is used to explore and analyze the traffic trajectory topic and evolution. Firstly, the spatial information is integrated into trajectory words, calculating the dynamic trajectory topic model based on dynamic analysis modeling and, consequently, correlating the evolution of the trajectory topic between adjacent time slices. Secondly, in the trajectory topic, a representative trajectory sequence is generated to overcome the problem of the trajectory topic model not considering the word order, based on the improved Markov Decision Process. Subsequently, a set of meaningful visual codes is designed to analyze the trajectory topic and its evolution through the parallel window visual model from a spatial-temporal perspective. Finally, a case evaluation shows that the proposed method is effective in analyzing potential trajectory movement patterns.
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Bonn, Mark A., Meehee Cho, and Hyemi Um. "The evolution of wine research." International Journal of Contemporary Hospitality Management 30, no. 1 (January 8, 2018): 286–312. http://dx.doi.org/10.1108/ijchm-09-2016-0521.
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Purpose Wine as a research topic continues to address a plethora of diverse contexts. In consideration of this scope and abundance of wine literature, this study aims to provide guidance for future meaningful contributions to this existing body of wine knowledge through a comprehensive scholarly review. Design/methodology/approach A total of 22 wine business, hospitality and tourism journals were selected and used to identify 739 refereed articles addressing wine-related topics over a 26-year period from 1990 to 2015. This was segmented using five wine research time frames, which were then separately investigated using content analysis and keyword network analysis. Findings Results support the importance for continued refinement of certain research areas to add understanding to wine research. In particular, the topics of marketing and tourism pertaining to wine research have fragmented into much more specialized sub-segments over this 26-year period. Research limitations/implications Limitations include generalizability of findings because of the study’s use of 22 journals, along with the selected 26-year period. Future research should examine other time periods using other publications in peripheral and in non-related areas to seek topics potentially and inadvertently overlooked by this process. Significant topics and trends regarding wine research were identified and classified according to time periods. Information has been provided for future directions and new research agendas. Originality/value Based upon an examination of time periods segmented by half-decades, keyword network analysis was used to explore wine research trends. Using keyword network analytics, this method for identifying networks between key words produced findings that have brought the literature regarding wine research to a current status allowing academics to gain insights into potential direction for future research needs.
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Lun, Weicheng, Qun Li, Zhi Zhu, and Can Zhang. "Routing Strategies for Isochronal-Evolution Random Matching Network." Entropy 25, no. 2 (February 16, 2023): 363. http://dx.doi.org/10.3390/e25020363.
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In order to abstract away a network model from some real-world networks, such as navigation satellite networks and mobile call networks, we proposed an Isochronal-Evolution Random Matching Network (IERMN) model. An IERMN is a dynamic network that evolves isochronally and has a collection of edges that are pairwise disjoint at any point in time. We then investigated the traffic dynamics in IERMNs whose main research topic is packet transmission. When a vertex of an IERMN plans a path for a packet, it is permitted to delay the sending of the packet to make the path shorter. We designed a routing decision-making algorithm for vertices based on replanning. Since the IERMN has a specific topology, we developed two suitable routing strategies: the Least Delay Path with Minimum Hop (LDPMH) routing strategy and the Least Hop Path with Minimum Delay (LHPMD) routing strategy. An LDPMH is planned by a binary search tree and an LHPMD is planned by an ordered tree. The simulation results show that the LHPMD routing strategy outperformed the LDPMH routing strategy in terms of the critical packet generation rate, number of delivered packets, packet delivery ratio, and average posterior path lengths.
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Bottinelli, Arianna, Marco Gherardi, and Marc Barthelemy. "Efficiency and shrinking in evolving networks." Journal of The Royal Society Interface 16, no. 154 (May 2019): 20190101. http://dx.doi.org/10.1098/rsif.2019.0101.
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Characterizing the spatio-temporal evolution of networks is a central topic in many disciplines. While network expansion has been studied thoroughly, less is known about how empirical networks behave when shrinking. For transportation networks, this is especially relevant on account of their connection with the socio-economical substrate, and we focus here on the evolution of the French railway network from its birth in 1840 to 2000, in relation to the country’s demographic dynamics. The network evolved in parallel with technology (e.g. faster trains) and under strong constraints, such as preserving a good population coverage and balancing cost and efficiency. We show that the shrinking phase that started in 1930 decreased the total length of the network while preserving efficiency and population coverage: efficiency and robustness remained remarkably constant while the total length of the network shrank by 50% between 1930 and 2000, and the total travel time and time-diameter decreased by more than 75% during the same period. Moreover, shrinking the network did not affect the overall accessibility with an average travel time that decreases steadily since its formation. This evolution leads naturally to an increase in transportation multimodality (such as a massive use of cars) and shows the importance of considering together transportation modes acting at different spatial scales. More generally, our results suggest that shrinking is not necessarily associated with a decay in performance and functions but can be beneficial in terms of design goals and can be part of the natural evolution of an adaptive network.
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Liu, Huailan, Zhiwang Chen, Jie Tang, Yuan Zhou, and Sheng Liu. "Mapping the technology evolution path: a novel model for dynamic topic detection and tracking." Scientometrics 125, no. 3 (September 14, 2020): 2043–90. http://dx.doi.org/10.1007/s11192-020-03700-5.
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AbstractIdentifying the evolution path of a research field is essential to scientific and technological innovation. There have been many attempts to identify the technology evolution path based on the topic model or social networks analysis, but many of them had deficiencies in methodology. First, many studies have only considered a single type of information (text or citation information) in scientific literature, which may lead to incomplete technology path mapping. Second, the number of topics in each period cannot be determined automatically, making dynamic topic tracking difficult. Third, data mining methods fail to be effectively combined with visual analysis, which will affect the efficiency and flexibility of mapping. In this study, we developed a method for mapping the technology evolution path using a novel non-parametric topic model, the citation involved Hierarchical Dirichlet Process (CIHDP), to achieve better topic detection and tracking of scientific literature. To better present and analyze the path, D3.js is used to visualize the splitting and fusion of the evolutionary path. We used this novel model to mapping the artificial intelligence research domain, through a successful mapping of the evolution path, the proposed method’s validity and merits are shown. After incorporating the citation information, we found that the CIHDP can be mapping a complete path evolution process and had better performance than the Hierarchical Dirichlet Process and LDA. This method can be helpful for understanding and analyzing the development of technical topics. Moreover, it can be well used to map the science or technology of the innovation ecosystem. It may also arouse the interest of technology evolution path researchers or policymakers.
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Wu, Jibing, Lianfei Yu, Qun Zhang, Peiteng Shi, Lihua Liu, Su Deng, and Hongbin Huang. "Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition." Complexity 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/9653404.
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The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.
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Wang, Ping, Yonghong Huang, Fei Tang, Hongtao Liu, and Yangyang Lu. "Overlapping Community Detection Based on Node Importance and Adjacency Information." Security and Communication Networks 2021 (December 31, 2021): 1–17. http://dx.doi.org/10.1155/2021/8690662.
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Detecting the community structure and predicting the change of community structure is an important research topic in social network research. Focusing on the importance of nodes and the importance of their neighbors and the adjacency information, this article proposes a new evaluation method of node importance. The proposed overlapping community detection algorithm (ILE) uses the random walk to select the initial community and adopts the adaptive function to expand the community. It finally optimizes the community to obtain the overlapping community. For the overlapping communities, this article analyzes the evolution of networks at different times according to the stability and differences of social networks. Seven common community evolution events are obtained. The experimental results show that our algorithm is feasible and capable of discovering overlapping communities in complex social network efficiently.
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Min, Chao, Qingyu Chen, Erjia Yan, Yi Bu, and Jianjun Sun. "Citation cascade and the evolution of topic relevance." Journal of the Association for Information Science and Technology 72, no. 1 (May 29, 2020): 110–27. http://dx.doi.org/10.1002/asi.24370.
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Das, Rosalina, Jessica Diaz, Sheela Dominguez, and Barry Issenberg. "514 Using text mining approaches to identify research trends – an exploratory analysis of Miami Clinical and Translational Science Institute publications from 2013-2021." Journal of Clinical and Translational Science 6, s1 (April 2022): 105. http://dx.doi.org/10.1017/cts.2022.308.
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OBJECTIVES/GOALS: The goal of this project was to perform an exploratory analysis of the research themes of scientific publications from the Miami Clinical and Translational Science Institute using text mining techniques and using bibliometric characterization and network analysis to further assess research trends. METHODS/STUDY POPULATION: Publications were identified from the Web of Science database using Miami CTSI grant numbers as search criteria for the period 2013-2021 and KL2 scholar publications. Following data pre-processing, topic modeling was performed using the Latent Dirichlet Allocation algorithm and cluster analysis in the R programming language. The resulting themes will be further analyzed by employing a citation-based impact assessment approach to identify trends over time. Network analysis of publications will be performed using the VOSviewer package to visualize publication networks using citation and co-authorship relations within each major theme and their evolution over time. Findings will be evaluated for alignment with institutional research strategy. RESULTS/ANTICIPATED RESULTS: About 400 CTSI publications from 2013-2021 to date were used for analysis. Twenty topics and five major research themes were identified among the Miami CTSI publications – neuroscience, cancer, community and public health, metabolics, and HIV/infectious diseases. Top keywords within each topic were aligned with the most frequent author-assigned keywords for that topic. The CTSI research themes were also well-aligned with the institutional vision for research and focus areas. Trends using citations and networks for each research theme are currently being analyzed and results will be included in the overall findings post analysis. DISCUSSION/SIGNIFICANCE: Text mining was successfully used in identifying topics and research themes for clinical and translational research publications of the Miami CTSI, and in combination with bibliometric characterization, will be helpful in shaping CTSI strategy and alignment with the universitys research priorities.
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Martins, Tânia, Alexandra Braga, Marisa R. Ferreira, and Vítor Braga. "Diving into Social Innovation: A Bibliometric Analysis." Administrative Sciences 12, no. 2 (April 30, 2022): 56. http://dx.doi.org/10.3390/admsci12020056.
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This paper aims to map and analyze the scientific production of social innovation, resulting in a contribution to the literature review and guidelines for future research. A bibliometric analysis was conducted to explore the trends on the topic. The primary objectives are (1) to identify how the literature defines the concept of social innovation and to track its evolution; (2) to measure productivity and identify key authors and scientific journals with the highest impact in the field and the association networks between their respective institutions and countries of origin; (3) to analyze and map citations, co-citations, and research topics to pinpoint the topics and dimensions related to social innovation in order to propose future research. Our paper clarifies the concept of social innovation, reports the progresses achieved within this research field, and measures the productivity on this specific topic.
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Floria, Sabina-Adriana, and Florin Leon. "A novel information diffusion model based on psychosocial factors with automatic parameter learning." Computer Science and Information Systems, no. 00 (2020): 50. http://dx.doi.org/10.2298/csis200415050f.
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Online social networks are the main choice of people to maintain their social relationships and share information or opinions. Estimating the actions of a user is not trivial because an individual can act spontaneously or be influenced by external factors. In this paper we propose a novel model for imitating the evolution of the information diffusion in a network as well as possible. Each individual is modeled as a node with two factors (psychological and sociological) that control its probabilistic transmission of information. The psychological factor refers to the node?s preference for the topic discussed, i.e. the information diffused. The sociological factor takes into account the influence of the neighbors? activity on the node, i.e. the gregarious behavior. A genetic algorithm is used to automatically tune the parameters of the model in order to fit the evolution of information diffusion observed in two real-world datasets with three topics. The reproduced diffusions show that the proposed model imitates the real diffusions very well.
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Alkhalec Tharwat, Muhammed E. Abd, Mohd Farhan Md Fudzee, Shahreen Kasim, Azizul Azhar Ramli, and Mohammed K. Ali. "Multi-objective NSGA-II based community detection using dynamical evolution social network." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (October 1, 2021): 4502. http://dx.doi.org/10.11591/ijece.v11i5.pp4502-4512.
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Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.
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Hu, Xinxin, Haotian Chen, Hongchang Chen, Xing Li, Junjie Zhang, and Shuxin Liu. "Mining Mobile Network Fraudsters with Augmented Graph Neural Networks." Entropy 25, no. 1 (January 11, 2023): 150. http://dx.doi.org/10.3390/e25010150.
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With the rapid evolution of mobile communication networks, the number of subscribers and their communication practices is increasing dramatically worldwide. However, fraudsters are also sniffing out the benefits. Detecting fraudsters from the massive volume of call detail records (CDR) in mobile communication networks has become an important yet challenging topic. Fortunately, Graph neural network (GNN) brings new possibilities for telecom fraud detection. However, the presence of the graph imbalance and GNN oversmoothing problems makes fraudster detection unsatisfactory. To address these problems, we propose a new fraud detector. First, we transform the user features with the help of a multilayer perceptron. Then, a reinforcement learning-based neighbor sampling strategy is designed to balance the number of neighbors of different classes of users. Next, we perform user feature aggregation using GNN. Finally, we innovatively treat the above augmented GNN as weak classifier and integrate multiple weak classifiers using the AdaBoost algorithm. A balanced focal loss function is also used to monitor the model training error. Extensive experiments are conducted on two open real-world telecom fraud datasets, and the results show that the proposed method is significantly effective for the graph imbalance problem and the oversmoothing problem in telecom fraud detection.
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Pepe, Alberto. "Socio-epistemic analysis of scientific knowledge production in little science research." tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 6, no. 2 (December 21, 2008): 134–45. http://dx.doi.org/10.31269/triplec.v6i2.84.
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The processes that drive knowledge production and dissemination in scientific environments are embedded within the social, technical, cultural and epistemic practices of the constituent research communities. This article presents a methodology to unpack specific social and epistemic dimensions of scientific knowledge production using, as a case study, the Center for Embedded Networked Sensing (CENS), a National Science Foundation “little science” research center involved in theoretical and applied work in the field of wireless communication and sensor networks. By analysis of its scholarly record, I construct a social network of coauthorship, linking individuals that have coauthored scholarly artifacts (journal articles and conference papers), and an epistemic network of topic co-occurrence, linking concepts and knowledge constructs in the same scholarly artifacts. This article reports on ongoing work directed at the study of the emergence and evolution of these networks of scientific interaction. I present some preliminary results and introduce a socio-epistemic method for an historical analysis of network co-evolution. I outline a research design to support further investigations of knowledge production in scientific circles.
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Katsurai, Marie, and Shunsuke Ono. "TrendNets: mapping emerging research trends from dynamic co-word networks via sparse representation." Scientometrics 121, no. 3 (October 18, 2019): 1583–98. http://dx.doi.org/10.1007/s11192-019-03241-6.
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Abstract Mapping the knowledge structure from word co-occurrences in a collection of academic papers has been widely used to provide insight into the topic evolution in an arbitrary research field. In a traditional approach, the paper collection is first divided into temporal subsets, and then a co-word network is independently depicted in a 2D map to characterize each period’s trend. To effectively map emerging research trends from such a time-series of co-word networks, this paper presents TrendNets, a novel visualization methodology that highlights the rapid changes in edge weights over time. Specifically, we formulated a new convex optimization framework that decomposes the matrix constructed from dynamic co-word networks into a smooth part and a sparse part: the former represents stationary research topics, while the latter corresponds to bursty research topics. Simulation results on synthetic data demonstrated that our matrix decomposition approach achieved the best burst detection performance over four baseline methods. In experiments conducted using papers published in the past 16 years at three conferences in different fields, we showed the effectiveness of TrendNets compared to the traditional co-word representation. We have made our codes available on the Web to encourage scientific mapping in all research fields.
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Shamsuddin, Siti Nurasyikin, Noriszura Ismail, and Nur Firyal Roslan. "What We Know about Research on Life Insurance Lapse: A Bibliometric Analysis." Risks 10, no. 5 (May 5, 2022): 97. http://dx.doi.org/10.3390/risks10050097.
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A lapsed policy is an insurance policy that has become inactive due to non-payment of premiums. The word “lapse” is an insurance topic that constantly evolves, proven by the recent increase in publications on this topic. The study explores the life insurance lapse decision through a comprehensive bibliometric analysis throughout the years, concentrating on publication trends; co-authorship networks among countries, authors, and scientific journals; and the field’s evolution. The research is based on the Scopus database. Ultimately, 178 documents were retrieved and analysed, demonstrating increased literature on insurance lapse from 1971 to 2021. The authors’ keyword co-occurrence network was also analysed for possible future directions of the field. Journals originating from the United Kingdom dominate the publication on life insurance lapsation. In contrast, an author from the United States is at the first rank in terms of the co-authorship network’s total link strength. The results may help researchers define the research objective and determine the aspects of the life insurance lapse for future research.
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Maria, Mariana Reis, Rosangela Ballini, and Roney Fraga Souza. "Evolution of Green Finance: A Bibliometric Analysis through Complex Networks and Machine Learning." Sustainability 15, no. 2 (January 5, 2023): 967. http://dx.doi.org/10.3390/su15020967.
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A fundamental structural transformation that must occur to break global temperature rise and advance sustainable development is the green transition to a low-carbon system. However, dismantling the carbon lock-in situation requires substantial investment in green finance. Historically, investments have been concentrated in carbon-intensive technologies. Nonetheless, green finance has blossomed in recent years, and efforts to organise this literature have emerged, but a deeper understanding of this growing field is needed. For this goal, this paper aims to delineate this literature’s existing groups and explore its heterogeneity. From a bibliometric coupling network, we identified the main groups in the literature; then, we described the characteristics of these articles through a novel combination of complex network analysis, topological measures, and a type of unsupervised machine learning technique called structural topic modelling (STM). The use of computational methods to explore literature trends is increasing as it is expected to be compatible with a large amount of information and complement the expert-based knowledge approach. The contribution of this article is twofold: first, identifying the most relevant articles in the network related to each group and, second, the most prestigious topics in the field and their contributions to the literature. A final sample of 3275 articles shows three main groups in the literature. The more mature is mainly related to the distribution of climate finance from the developed to the developing world. In contrast, the most recent ones are related to climate financial risks, green bonds, and the insertion of financial development in energy-emissions-economics models. Researchers and policy-makers can recognise current research challenges and make better decisions with the help of the central research topics and emerging trends identified from STM. The field’s evolution shows a clear movement from an international perspective to a nationally-determined discussion on finance to the green transition.
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Wei, Ling, Haiyun Xu, Zhenmeng Wang, Kun Dong, Chao Wang, and Shu Fang. "Topic Detection Based on Weak Tie Analysis: A Case Study of LIS Research." Journal of Data and Information Science 1, no. 4 (September 1, 2017): 81–101. http://dx.doi.org/10.20309/jdis.201626.
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AbstractPurposeBased on the weak tie theory, this paper proposes a series of connection indicators of weak tie subnets and weak tie nodes to detect research topics, recognize their connections, and understand their evolution.Design/methodology/approachFirst, keywords are extracted from article titles and preprocessed. Second, high-frequency keywords are selected to generate weak tie co-occurrence networks. By removing the internal lines of clustered sub-topic networks, we focus on the analysis of weak tie subnets’ composition and functions and the weak tie nodes’ roles.FindingsThe research topics’ clusters and themes changed yearly; the subnets clustered with technique-related and methodology-related topics have been the core, important subnets for years; while close subnets are highly independent, research topics are generally concentrated and most topics are application-related; the roles and functions of nodes and weak ties are diversified.Research limitationsThe parameter values are somewhat inconsistent; the weak tie subnets and nodes are classified based on empirical observations, and the conclusions are not verified or compared to other methods.Practical implicationsThe research is valuable for detecting important research topics as well as their roles, interrelations, and evolution trends.Originality/valueTo contribute to the strength of weak tie theory, the research translates weak and strong ties concepts to co-occurrence strength, and analyzes weak ties’ functions. Also, the research proposes a quantitative method to classify and measure the topics’ clusters and nodes.
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Chen, Xiaolin, Qixing Qu, Chengxi Wei, and Shudong Chen. "Cross-Domain Transfer Learning Prediction of COVID-19 Popular Topics Based on Knowledge Graph." Future Internet 14, no. 4 (March 24, 2022): 103. http://dx.doi.org/10.3390/fi14040103.
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The significance of research on public opinion monitoring of social network emergencies is becoming increasingly important. As a platform for users to communicate and share information online, social networks are often the source of public opinion about emergencies. Considering the relevance and transmissibility of the same event in different social networks, this paper takes the COVID-19 outbreak as the background and selects the platforms Weibo and TikTok as the research objects. In this paper, first, we use the transfer learning model to apply the knowledge obtained in the source domain of Weibo to the target domain of TikTok. From the perspective of text information, we propose an improved TC-LDA model to measure the similarity between the two domains, including temporal similarity and conceptual similarity, which effectively improves the learning effect of instance transfer and makes up for the problem of insufficient sample data in the target domain. Then, based on the results of transfer learning, we use the improved single-pass incremental clustering algorithm to discover and filter popular topics in streaming data of social networks. Finally, we build a topic knowledge graph using the Neo4j graph database and conduct experiments to predict the evolution of popular topics in new emergencies. Our research results can provide a reference for public opinion monitoring and early warning of emergencies in government departments.
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Han, Xiaoyao. "Evolution of research topics in LIS between 1996 and 2019: an analysis based on latent Dirichlet allocation topic model." Scientometrics 125, no. 3 (October 10, 2020): 2561–95. http://dx.doi.org/10.1007/s11192-020-03721-0.
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AbstractThis study investigated the evolution of library and information science (LIS) by analyzing research topics in LIS journal articles. The analysis is divided into five periods covering the years 1996–2019. Latent Dirichlet allocation modeling was used to identify underlying topics based on 14,035 documents. An improved data-selection method was devised in order to generate a dynamic journal list that included influential journals for each period. Results indicate that (a) library science has become less prevalent over time, as there are no top topic clusters relevant to library issues since the period 2000–2005; (b) bibliometrics, especially citation analysis, is highly stable across periods, as reflected by the stable subclusters and consistent keywords; and (c) information retrieval has consistently been the dominant domain with interests gradually shifting to model-based text processing. Information seeking and behavior is also a stable field that tends to be dispersed among various topics rather than presented as its own subject. Information systems and organizational activities have been continuously discussed and have developed a closer relationship with e-commerce. Topics that occurred only once have undergone a change of technological context from the networks and Internet to social media and mobile applications.
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Chen, Wei, Panlong Yang, Wei Zhao, and Linna Wei. "Improved Ant Lion Optimizer for Coverage Optimization in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2022 (August 16, 2022): 1–15. http://dx.doi.org/10.1155/2022/8808575.
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Coverage optimization is an important research topic in wireless sensor networks (WSNs). By studying the coverage optimization problem, the coverage rate of the network can be improved, and the number of redundant sensors can be reduced. In order to improve the coverage performance of wireless sensor networks, we propose an improved ant lion optimizer (IALO) to solve the coverage optimization problem in a WSN. Firstly, we give a network coverage optimization model with the objective of maximizing network coverage rate. Secondly, we alternately execute Cuckoo Search (CS) and Cauchy mutation to update the positions of the ants to enhance population diversity and accelerate convergence speed. Then, we introduce differential evolution (DE) to mutate the population of antlions to improve the convergence accuracy of our algorithm. We compare IALO with the original ant lion optimizer (ALO) and other algorithms on 9 benchmark functions to verify its effectiveness. Finally, IALO is applied to the coverage optimization in wireless sensor networks. Simulation results show that, compared with previous works, IALO provides higher coverage rate, makes the sensor distribution more uniform, and effectively reduces the deployment cost.
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Mohammed Ali, Salim A., and Emad H. Al-Hemairy. "MINIMIZING E2E DELAY IN V2X OVER CELLULAR NETWORKS: REVIEW AND CHALLENGES." Iraqi Journal of Information & Communications Technology 2, no. 4 (February 23, 2020): 31–42. http://dx.doi.org/10.31987/ijict.2.4.79.
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V2X (Vehicle-to-Everything) evolution over cellular networks has been an excelling topic with the advances of high throughput and low latency LTE networks, and the introduction of 5G networks. According to recent researches, obtaining acceptable End-to-End (E2E) delay has been a challenging design process since data travels through several steps from the originating source to the data center and vice versa. V2X latency comprises mainly of three levels: source processing, cellular network, and data center processing. Delay reduction can be achieved on the three levels. However, many conventional solutions have not reached the required and acceptable range of latency to enable V2X communication over cellular networks. In this paper, a general review of challenges to make V2X feasible on cellular network has been discussed, and the proposed solutions in the literature has been introduced. As a conclusion, a various types of aiding tools to design and test V2X tools are given, so that a right path should be taken to consider challenges and improving design metrics.
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Kovács, László, Katalin Orosz, and Peter Pollner. "Growing Networks – Modelling the Growth of Word Association Networks for Hungarian and English." Investigationes Linguisticae, no. 45 (December 30, 2021): 67–82. http://dx.doi.org/10.14746/il.2021.45.5.
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In the new era of information and communication technology, the representation of information is of increasing importance. Knowing how words are connected to each other in the mind and what processes facilitate the creation of connections could result in better optimized applications, e.g. in computer aided education or in search engines.
This paper models the growth process of a word association database with an algorithm. We present the network structure of word associations for an agglutinative language and compare it with the network of English word associations. Using the real-world data so obtained, we create a model that reproduces the main features of the observed growth process and show the evolution of the network. The model describes the growth of the word association data as a mixture of a topic based process and a random process.
The model makes it possible to gain insight into the overall processes which are responsible for creating an interconnected mental lexicon.
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Du, Xiaojun, Fei Feng, and Wei Lv. "Bibliometric Overview of Organizational Legitimacy Research." SAGE Open 12, no. 2 (April 2022): 215824402210995. http://dx.doi.org/10.1177/21582440221099524.
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In recent years, the evaluator’s perspective of legitimacy theory appears to be a hot topic. We provide an overview by conducting a bibliometric review in this field. Based on the data during 1995 to 2020 from the Web of Science Core Collection database, this study investigates the present status, the evolution of the evaluator’s perspective, thematic change, and intellectual turning points via HistCite, CiteSpace, etc. Results demonstrate that the evaluator’s perspective is experiencing exponential growth. Suchman MC is the most cited author; While Palazzo G is the most productive. Academy of Management Review ranks first in citation, and Journal of Business Ethics is the most productive. The University of Lausanne enjoys the highest citation, and Copenhagen Business School publishes the most articles. The USA ranks first in terms of citation and publication. Author collaboration network is not close, while institutional collaboration and national collaboration networks are relatively dense. Highly-cited articles show the evolution of the evaluator’s perspective, and five evolution paths are found. The main areas are concentrated on signaling theory, business ethics, CSR communication, reputation, environmental disclosure, etc; 14 references act as intellectual turning points. The findings provide a panorama for researchers interested in this topic and help to better understand future research direction.
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Kang, Hyo-Jin, Jieun Han, and Gyu Hyun Kwon. "Determining the Intellectual Structure and Academic Trends of Smart Home Health Care Research: Coword and Topic Analyses." Journal of Medical Internet Research 23, no. 1 (January 21, 2021): e19625. http://dx.doi.org/10.2196/19625.
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Background With the rapid development of information and communication technologies, smart homes are being investigated as effective solutions for home health care. The increasing academic attention on smart home health care has primarily been on the development and application of smart home technologies. However, comprehensive studies examining the general landscape of diverse research areas for smart home health care are still lacking. Objective This study aims to determine the intellectual structure of smart home health care in a time series by conducting a coword analysis and topic analysis. Specifically, it investigates (1) the intellectual basis of smart home health care through overall academic status, (2) the intellectual foci through influential keywords and their evolutions, and (3) intellectual trends through primary topics and their evolutions. Methods Analyses were conducted in 5 steps: (1) data retrieval from article databases (Web of Science, Scopus, and PubMed) and the initial dataset preparation of 6080 abstracts from the year 2000 to the first half of 2019; (2) data preprocessing and refinement extraction of 25,563 words; (3) a descriptive analysis of the overall academic status and period division (ie, 4 stages of 3-year blocks); (4) coword analysis based on word co-occurrence networks for the intellectual foci; and (5) topic analysis for the intellectual trends based on latent Dirichlet allocation (LDA) topic modeling, word-topic networks, and researcher workshops. Results First, regarding the intellectual basis of smart home health care, recent academic interest and predominant journals and research domains were verified. Second, to determine the intellectual foci, primary keywords were identified and classified according to the degree of their centrality values. Third, 5 themes pertaining to the topic evolution emerged: (1) the diversification of smart home health care research topics; (2) the shift from technology-oriented research to technological convergence research; (3) the expansion of application areas and system functionality of smart home health care; (4) the increased focus on system usability, such as service design and experiences; and (5) the recent adaptation of the latest technologies in health care. Based on these findings, the pattern of technology diffusion in smart home health care research was determined as the adaptation of technologies, the proliferation of application areas, and an extension into system design and service experiences. Conclusions The research findings provide academic and practical value in 3 aspects. First, they promote a comprehensive understanding of the smart home health care domain by identifying its multifaceted intellectual structure in a time series. Second, they can help clinicians discern the development and dispersion level of their respective disciplines. Third, the pattern of technology diffusion in smart home health care could help scholars comprehend current and future research trends and identify research opportunities based on upcoming research waves of newly adapted technologies in smart home health care.
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Zhao, Yaning, and Chengyi Xia. "Crash behavior modeling and analysis on two interdependent networks." Modern Physics Letters B 35, no. 11 (February 17, 2021): 2150182. http://dx.doi.org/10.1142/s0217984921501827.
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In real-world systems, one random failure or targeted attack may lead to the cascading destruction within a system, and even create the systemic collapse among multiple correlated ones. With the help of network science, modeling the cascade failure on complex networks has become a challenging topic. In this paper, we put forward an improved cascading model on two interdependent networks to further explore the impact of the specific detachment logic on the system collapse, where the distinct condition to leave the system for a pair of nodes exists on the two-layered networks. Meanwhile, once the detachment logic is satisfied, two different criteria are adopted to determine whether this pair of nodes will leave the interdependent networks. Through extensive numerical simulations, we analyze the effects of detachment logic, network topology, departure criteria and nodal coupling relationship between layers in detail. It is found that in our detachment logic, both criteria will render the whole system to exhibit the phenomenon of pseudo-steady state and sudden collapse. In particular, two critical thresholds to characterize the evolution of system crashing emerge, which is different from previous findings under other detachment logic conditions on two-layered networks. Current results are conducive to further understanding the crashing behaviors of interdependent networks and designing the more robust networked systems in practice.
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Trandafili, Evis, and Marenglen Biba. "A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks." International Journal of E-Business Research 9, no. 1 (January 2013): 36–53. http://dx.doi.org/10.4018/jebr.2013010103.
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Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.
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Thanh Vu, Simon Nam, Mads Stege, Peter Issam El-Habr, Jesper Bang, and Nicola Dragoni. "A Survey on Botnets: Incentives, Evolution, Detection and Current Trends." Future Internet 13, no. 8 (July 31, 2021): 198. http://dx.doi.org/10.3390/fi13080198.
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Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research.
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Zhou, Hou-kui, Hui-min Yu, and Roland Hu. "Topic discovery and evolution in scientific literature based on content and citations." Frontiers of Information Technology & Electronic Engineering 18, no. 10 (October 2017): 1511–24. http://dx.doi.org/10.1631/fitee.1601125.
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Reza Mazandarani, Mohammad, and Marcelo Royo Vela. "Firms’ internationalization through clusters: A keywords bibliometric analysis of 152 top publications in the period 2009-2018." Cuadernos de Gestión 22, no. 1 (February 10, 2022): 229–42. http://dx.doi.org/10.5295/cdg.211483mr.
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While countless studies on the role of clusters in regional economic developments and business performance have been done, some disadvantages and limitations also have been identified. Limitations such as small local markets, limited resources, isolation, and over-independence which lead companies to a lock-in state regarding knowledge and innovation can be solved by means of internationalization or foreign market expansion. Therefore, the internationalization of clusters still needs more attention. Furthermore, by conducting a bibliometric study based on the keywords from previous research, this investigation intends to identify the principal and most influential items, their relationship, and evolution, related to this topic. This work listed the top 30 journals based on SCImago Rank (SJR) in the category of Marketing, Management, and Internationalization, to select authoritative papers on the topic of internationalization of clusters. Based on the title, time of publication (2009 to 2018), and content, 152 articles were selected. Using SciMAT software, all 584 keywords used in these articles were first categorized and then examined in terms of the networks between them, their h-index, centrality, density, and their evolution over the course of ten years (divided into two five-year periods). The results show that in the first five years, keywords such as, Network, Location-distance, and Global, have been at the core of this research topic. While in the second five year period, these keywords have yielded their position to new subjects such as Social capital, Cluster-type, and Development. Moreover, this research correspondingly confirms that the keyword bibliometric analysis produces similar results to a quantitative content analysis. These results therefore open the door to updated research questions for further and better development of the topic.
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Zhao, Weize, Lukasz Walasek, and Gordon D. A. Brown. "The Evolution of Polarization in Online Conversation: Twitter Users’ Opinions about the COVID-19 Pandemic Become More Politicized over Time." Human Behavior and Emerging Technologies 2023 (July 5, 2023): 1–14. http://dx.doi.org/10.1155/2023/9094933.
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Political polarization on social media has been extensively studied. However, most research has examined polarization about topics that have preexisting associations with ideology, while few studies have tracked the onset of polarization about novel topics or the evolution of polarization over a prolonged period. The occurrence of COVID-19 provides a unique opportunity to study whether social media discourse about a novel event becomes increasingly polarized along ideological lines over time. This paper analyzes trends in Twitter polarization in relation to COVID-19 and other geopolitical events of 2020. The first two studies use topic analysis to examine the evolving difference over time in discussions of COVID-19 and other topics by liberals and conservatives on social media. COVID-19-related polarization is initially absent but gradually increases over time, in contrast to polarization related to other events. A third study examines structural polarization in retweet networks and finds that the frequency of counterpartisan retweets reduces over time. Across all three studies, we find evidence that Twitter discussion of COVID-19 has become more polarized over time.
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Wan, Huaiyu, Yutao Zhang, Jing Zhang, and Jie Tang. "AMiner: Search and Mining of Academic Social Networks." Data Intelligence 1, no. 1 (March 2019): 58–76. http://dx.doi.org/10.1162/dint_a_00006.
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AMiner is a novel online academic search and mining system, and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks formed by authors, papers, conferences, journals and organizations. The system is subsequently able to extract researchers’ profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation. Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search. In addition, AMiner offers a set of researcher-centered functions, including social influence analysis, relationship mining, collaboration recommendation, similarity analysis, and community evolution. The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions.
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Maskat, Ruhaila, Shazlyn Milleana Shaharudin, Deden Witarsyah, and Hairulnizam Mahdin. "A Survey on Forms of Visualization and Tools Used in Topic Modelling." JOIV : International Journal on Informatics Visualization 7, no. 2 (May 5, 2023): 517. http://dx.doi.org/10.30630/joiv.7.2.1313.
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In this paper, we surveyed recent publications on topic modeling and analyzed the forms of visualizations and tools used. Expectedly, this information will help Natural Language Processing (NLP) researchers to make better decisions about which types of visualization are appropriate for them and which tools can help them. This could also spark further development of existing visualizations or the emergence of new visualizations if a gap is present. Topic modeling is an NLP technique used to identify topics hidden in a collection of documents. Visualizing these topics permits a faster understanding of the underlying subject matter in terms of its domain. This survey covered publications from 2017 to early 2022. The PRISMA methodology was used to review the publications. One hundred articles were collected, and 42 were found eligible for this study after filtration. Two research questions were formulated. The first question asks, "What are the different forms of visualizations used to display the result of topic modeling?" and the second question is "What visualization software or API is used? From our results, we discovered that different forms of visualizations meet different purposes of their display. We categorized them as maps, networks, evolution-based charts, and others. We also discovered that LDAvis is the most frequently used software/API, followed by the R language packages and D3.js. The primary limitation of this survey is it is not exhaustive. Hence, some eligible publications may not be included.
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Kannan, Yamini, and Dharika Kapil. "Implementing Machine Learning Algorithms for Predictive Network Maintenance in 5G and Beyond Networks." International Journal of Wireless & Mobile Networks 16, no. 1/2 (April 28, 2024): 01–11. http://dx.doi.org/10.5121/ijwmn.2024.16201.
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With the evolution of fifth generation (5G) network technologies, network maintenance strategies have become increasingly complex, necessitating the use of predictive analysis enabled by Machine Learning (ML) algorithms. This paper emphasizes exploring how ML algorithms can further enhance predictive maintenance in 5G and future networks. It reviews the current literature on this interdisciplinary topic, identifying key ML models such as Decision Trees, Neural Networks, and Support Vector Machines, and discussing their benefits and limitations. Special attention is given to the methodologies in applying these models, handling of data stages, and the training process. Major challenges in implementing ML in the context of network maintenance, such as data privacy, data gathering, model training, and generalizability, are discussed. Furthermore, the research aims to go beyond predicting maintenance needs to introduce a proactive approach in improving overall network performance and pre-empting potential issues based on ML predictions. The paper also discusses possible future trends including advancements in ML algorithms, Automated Machine Learning (AutoML), Explainable AI, and others. The objective is to provide a comprehensive understanding of the current ML-based predictive maintenance field and outline possibilities for future research. The study finds that the application of ML algorithms continues to show promise in transforming the landscape of network management by improving predictive maintenance and proactive performance enhancement strategies. It remains a challenging yet important area in the context of 5G networks.
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Supriyadi, Edouard Aryadi, and Luluk Nihayati. "Analisis bibliometrik pada Pengembangan Desa Wisata." JURNAL KAJIAN PARIWISATA DAN BISNIS PERHOTELAN 4, no. 1 (April 25, 2023): 11–22. http://dx.doi.org/10.24036/jkpbp.v4i1.61572.
Full textAbstract:
Studies on the development of tourism villages have been conducted by many researchers in different aspects and locations. This study looks at the development of tourist villages from 2018 to 2022. We used bibliometric analysis by combining two well-known software: Publish and Perish (PoP) and VoS viewer. Data was collected through Google Scholar-based PoP over a 5-year period from 2018 to 2022. The data was generated from Indonesian articles generated from the database using keywords specific to tourism village development. We limited the maximum number of results to 100 articles, then refined by selecting relevant sources and found 70 selected articles. The results showed that existing research on tourism village development on various topics and research networks were visualized. Through collaborative networking, the results can be useful for academic researchers to help them understand the evolution of tourism village development research, identify the underlying themes and assist the development of current tourism village development concepts. This paper is one of the existing papers that provides an understanding of tourism village development as an interesting research topic by examining its development through bibliometric analysis. Based on network visualization with the keyword that appears most often is the development of tourist villages. When viewed from Density Visualization, tourist village, development, village development, village and in the development of tourist villages show that the topic is being the center of research. Then for topics such as community empowerment through tourism villages provides a clue that this study material is still small and very less studied.