Статті в журналах: "Social network system"

1

Westaby, James D., and Adam K. Parr. "Network Goal Analysis of Social and Organizational Systems: Testing Dynamic Network Theory in Complex Social Networks." Journal of Applied Behavioral Science 56, no. 1 (October 24, 2019): 107–29. http://dx.doi.org/10.1177/0021886319881496.

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Grounded in dynamic network theory, this study examined network goal analysis (NGA) to understand complex systems. NGA provides new insights by inserting goal nodes into social networks. Goal nodes can also represent missions, objectives, or desires, thus having wide applicability. The theory ties social networks to goal nodes through a parsimonious set of social network role linkages, such as independent goal striving, system supporting, feedback, goal preventing, supportive resisting, and system negating (i.e., those who are upset with others in the pursuit). Moreover, we extend the theory’s system reactance role linkage to better account for constructive conflicts. Two complex systems were examined: a team’s mission and an individual’s work project. In support of dynamic network theory, using the Quadratic Assignment Procedure, results demonstrated significant shared goal striving, system supporting, and shared connections between goal striving and system supporting. These findings manifest what we coin as multipendence: Systems having some actions independently involved with goals, while others are dependently involved in the associated network. NGA also demonstrated that the goal nodes manifested strong betweenness centrality, indicating that goal striving and feedback links were connecting entities across the wider system. Strategies to plan network goal interventions are illustrated with implications for practice.

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2

Cadima, Rita, Carlos Ferreira, Josep Monguet, Jordi Ojeda, and Joaquin Fernandez. "Promoting social network awareness: A social network monitoring system." Computers & Education 54, no. 4 (May 2010): 1233–40. http://dx.doi.org/10.1016/j.compedu.2009.11.009.

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3

Letch, Nick. "Ecologies of interests in social information systems for social benefit." Information Technology & People 29, no. 1 (March 7, 2016): 14–30. http://dx.doi.org/10.1108/itp-09-2014-0218.

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Purpose – The purpose of this paper is to explore a class of social information systems which are purposefully designed to address wider social objectives. Specifically, the paper investigates the embedding of ICTs into the wider networks of social policy action and explores issues associated with the integration of social information systems into complex problem domains. Design/methodology/approach – A case study of a social information system and its integration into networks of actors with an interest in the underlying social concern is presented. The system under analysis is first described in terms of the emerging characteristics used to define this class of social information system. The wider policy network in which the social information system is implemented is then described and the integration of the social information system into the wider network is discussed. Findings – The case study illustrates that for complex social problems, there can be multiple interests embedded in an ecology of sub-networks. Each sub-network can make use of the social information system in different ways which creates difficulties in the social information system gaining sufficient legitimacy to be institutionalised into the wider policy network. Originality/value – The paper extends understanding of social information systems by proposing that a class of social information systems are developed to pursue human benefit. Recognising the context in which these systems are integrated as an ecology of interests, shifts the focus of social information systems design from examining the requirements of a relatively homogenous community of actors to understanding how social information systems can be developed to enable information exchange within and across heterogeneous communities.

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4

Gilles, Robert, Tabitha James, Reza Barkhi, and Dimitrios Diamantaras. "Simulating Social Network Formation." International Journal of Virtual Communities and Social Networking 1, no. 4 (October 2009): 1–20. http://dx.doi.org/10.4018/jvcsn.2009092201.

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Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

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5

Liu, Xiaozhong, Tian Xia, Yingying Yu, Chun Guo, and Yizhou Sun. "Cross Social Media Recommendation." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (August 4, 2021): 221–30. http://dx.doi.org/10.1609/icwsm.v10i1.14714.

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The proliferation of rich social media data revolutionizes the way people perceive and understand the world. Unfortunately, so far, there does not exist a single social media system that efficiently globalizes users around the world. Two well-known social media systems, Twitter and Facebook, are strictly blocked in mainland China for political reasons, which means 21.97% of Internet users are excluded from these systems. Similarly, the second-largest microblogging system in the world, Sina Weibo, features a default system language of Chinese, which rules out many users from other countries. This creates what we call language, network, and culture bubbles. As a result, if we are interested in modeling the knowledge of the world, all research findings based on a single social media system (within a bubble) can be biased, and the social networks or knowledge networks generated from a single system or social community cannot fully represent people from around the world. In this study, we generate a pseudo-social heterogeneous network - Pseudo Global Social Media Network (PGSMN), which bridges the topics of Twitter and Weibo. On this network, all Weibo and Twitter nodes are interconnected via an interim knowledge layer, and user or topic nodes from Twitter can randomly walk to the nodes on Weibo (via different kinds of paths), and vice versa, which enables cross-network information recommendation and knowledge globalization.

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6

Chen, Chaochao, Xiaolin Zheng, Mengying Zhu, and Litao Xiao. "Recommender System with Composite Social Trust Networks." International Journal of Web Services Research 13, no. 2 (April 2016): 56–73. http://dx.doi.org/10.4018/ijwsr.2016040104.

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The development of online social networks has increased the importance of social recommendations. Social recommender systems are based on the idea that users who are linked in a social trust network tend to share similar interests. Thus, how to build an accurate social trust network will greatly affect recommendation performance. However, existing trust-based recommender approaches do not fully utilize social information to build rational trust networks and thus have low prediction accuracy and slow convergence speed. In this paper, the authors propose a composite trust-based probabilistic matrix factorization model, which is mainly composed of two steps: In step 1, the existing explicit trust network and the inferred implicit trust network are used to build a composite trust network. In step 2, the composite trust network is used to minimize both the rating difference and the trust difference between the true value and the inferred value. Experiments based on an Epinions dataset show that the authors' approach has significantly higher prediction accuracy and convergence speed than traditional collaborative filtering technology and the state-of-the-art trust-based recommendation approaches.

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7

Schmälzle, Ralf, Matthew Brook O’Donnell, Javier O. Garcia, Christopher N. Cascio, Joseph Bayer, Danielle S. Bassett, Jean M. Vettel, and Emily B. Falk. "Brain connectivity dynamics during social interaction reflect social network structure." Proceedings of the National Academy of Sciences 114, no. 20 (May 2, 2017): 5153–58. http://dx.doi.org/10.1073/pnas.1616130114.

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Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants’ friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics.

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8

Kim, Jaehyuk, and Yeunwoong Kyung. "Activity-based Friend Recommendation System (ARS) in Location-based Social Network." Webology 19, no. 1 (January 20, 2022): 4482–90. http://dx.doi.org/10.14704/web/v19i1/web19295.

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Common friend and place recommendation services in Location-based Social Network (LBSN) is based on user’s location tracking. However, since each user can do different activities even in the same place, location data is not enough to provide accurate recommendation for LSBN. To address this problem, Activity-based friend and place Recommendation System (ARS) is proposed. ARS considers two additional factors to improve recommendation accuracy: time and activity. ARS collects the time-related activity and location data from users through the developed scheduler application and then performs the recommendation for users based on the calculated similarity among them. Performance evaluation shows that ARS can provide accurate recommendation between users who have similar activity and location patterns according to time.

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9

Kazienko, Przemysław, Katarzyna Musial, and Tomasz Kajdanowicz. "Multidimensional Social Network in the Social Recommender System." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 41, no. 4 (July 2011): 746–59. http://dx.doi.org/10.1109/tsmca.2011.2132707.

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10

Sperlì, Giancarlo, Flora Amato, Fabio Mercorio, Mario Mezzanzanica, Vincenzo Moscato, and Antonio Picariello. "A Social Media Recommender System." International Journal of Multimedia Data Engineering and Management 9, no. 1 (January 2018): 36–50. http://dx.doi.org/10.4018/ijmdem.2018010103.

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Social media recommendation differs from traditional recommendation approaches as it needs considering not only the content information and users' similarities, but also users' social relationships and behavior within an online social network as well. In this article, a recommender system – designed for big data applications – is used for providing useful recommendations in online social networks. The proposed technique represents a collaborative and user-centered approach that exploits the interactions among users and generated multimedia contents in one or more social networks in a novel and effective way. The experiments performed on data collected from several online social networks show the feasibility of the approach towards the social media recommendation problem.

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11

Jeong, Ok-Ran. "Social Network based Podcast Search System." Journal of Korean Society for Internet Information 14, no. 2 (April 30, 2013): 35–43. http://dx.doi.org/10.7472/jksii.2013.14.2.35.

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12

Park, Taesoo, and Ok-Ran Jeong. "Social Network Based Music Recommendation System." Journal of Internet Computing and Services 16, no. 6 (December 31, 2015): 133–41. http://dx.doi.org/10.7472/jksii.2015.16.6.133.

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13

Ye, Yanming, Jianwei Yin, and Yueshen Xu. "Social Network Supported Process Recommender System." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/349065.

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Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.

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14

Ali, Hassan Najam. "Social Network Based Learning Management System." IOSR Journal of Computer Engineering 3, no. 2 (2012): 18–23. http://dx.doi.org/10.9790/0661-0321823.

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15

Xia, Zhongxiu, Weiyu Zhang, and Ziqiang Weng. "Social Recommendation System Based on Hypergraph Attention Network." Computational Intelligence and Neuroscience 2021 (November 5, 2021): 1–12. http://dx.doi.org/10.1155/2021/7716214.

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In recent years, due to the rise of online social platforms, social networks have more and more influence on our daily life, and social recommendation system has become one of the important research directions of recommendation system research. Because the graph structure in social networks and graph neural networks has strong representation capabilities, the application of graph neural networks in social recommendation systems has become more and more extensive, and it has also shown good results. Although graph neural networks have been successfully applied in social recommendation systems, their performance may still be limited in practical applications. The main reason is that they can only take advantage of pairs of user relations but cannot capture the higher-order relations between users. We propose a model that applies the hypergraph attention network to the social recommendation system (HASRE) to solve this problem. Specifically, we take the hypergraph’s ability to model high-order relations to capture high-order relations between users. However, because the influence of the users’ friends is different, we use the graph attention mechanism to capture the users’ attention to different friends and adaptively model selection information for the user. In order to verify the performance of the recommendation system, this paper carries out analysis experiments on three data sets related to the recommendation system. The experimental results show that HASRE outperforms the state-of-the-art method and can effectively improve the accuracy of recommendation.

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16

Niese, Bethany, and Sharath Sasidharan. "Getting Social." International Journal of Knowledge Management 18, no. 1 (January 1, 2022): 1–23. http://dx.doi.org/10.4018/ijkm.313956.

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Knowledge acquired by end users through their social networks facilitates optimal use of a newly implemented enterprise system. Existing research has conceptualized end users as being the only actors within such networks. Knowledge ties between actors have been treated as unidimensional. The actor-network theory emphasizes the role of all actors in influencing networking outcomes; hence, this study proposes an expanded multimodal social network that includes four institutionally mandated knowledge actors: the technology champions, the help desk, the service desk, and the shared inbox. Knowledge ties are treated as bidimensional through incorporating both technical and business process knowledge. Data collected from an enterprise resource planning system implementation validated this approach; end users sourced knowledge from other end users and the institutionally mandated network actors based on contextual requirements. End user performance outcomes were significantly associated with knowledge source and knowledge dimension.

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17

Virmani, Charu, Dimple Juneja, and Anuradha Pillai. "Design of a Novel Query System for Social Network." Journal of Information Technology Research 12, no. 2 (April 2019): 175–93. http://dx.doi.org/10.4018/jitr.2019040110.

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User intention and nature of network plays a vital role towards the quality of response received as the result of any user query. Therefore, the need of system understanding the user's intent and network dynamism as well is highly apparent. The proposed query processing and analysing system (QPAS) for social networks is based on extracting user's intent from various social networks using existing NLP techniques. It fetches the information and further employs hybrid ensemble k-means hierarchical agglomerative clustering (HEKHAC) and modified Bitonic sort to improve the responses. The proposed approach offers an edge over other mechanisms as it not only retrieves more user-centric results as compared to traditional way of keyword-based searching but also in timely manner as well. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a noteworthy revolution scoring up to precision and recall respectively.

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18

Filippov, Rodion, Yuriy Leonov, Aleksandr Kuzmenko, and Timofey Shestakov. "Analysis of Social Networks Using an Information and Analytical System." SHS Web of Conferences 110 (2021): 05009. http://dx.doi.org/10.1051/shsconf/202111005009.

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The subject of the study is the analysis of social networks and the construction of an information and analytical system to automate data monitoring and mining. Modern social network analysis systems are reviewed, and the distinguishing features of these systems are given. Various methods of social network analysis and tasks that can be solved using these methods are described. The effectiveness of the methods to determine the text sentiment is compared.

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19

Zhia Sheng, Dalian Wu, Isredza Rahmi A. Hamid, and Hannani Aman. "Multilevel Authentication for Social Network." JOIV : International Journal on Informatics Visualization 2, no. 3-2 (June 6, 2018): 220. http://dx.doi.org/10.30630/joiv.2.3-2.146.

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Nowadays, social network plays a vital role in communication. Normally user used social networks in daily activities. However, this left all online users susceptible to misuse and abuse. Recently, there has been a remarkable growth in number of hacking as well. Once the computer is online, anyone can have access to the network. Therefore, we proposed a secure social network site called SocialBook where users can post status, photos and connect with friends. This system is developed using PHP programming language and Iterative and Incremental Development methodology. The purpose of developing this system is to solve unsecured login accounts and lack of user authentication problem. This system applies the secret question procedure when the user wants to change their account password. Moreover, SocialBook use idle session timeout mechanism for additional security. When the user is idle for ten minutes, they will be logged out automatically. So, the user will be less worry about their account from being hacked and the identity impersonation by unauthorized user.

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20

Wang, Min, Hong Xie, Wei Gao, and Nan Sun. "Design and Implementation of Multimodal SNS Information Integration System." Applied Mechanics and Materials 719-720 (January 2015): 851–56. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.851.

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With the popularity of the network and mobile Internet, numerous social network platforms appeared in the world. Also, there have been a large number of users using a plurality of social network platforms at the same time. When these users use multiple social network platforms, they need to overcome many difficulties such as switching social network client applications frequently and so on. This article takes consideration of the needs of the multiple social networks’ users, proposing a solution to solve multi-barrier problem, and demonstrating the feasibility and effectiveness of the method by actual cases.

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21

Zhao, Qing Jian, and Zuo Min Wen. "Complex Social-Ecological Systems Network：New Perspective on the Sustainability." Advanced Materials Research 361-363 (October 2011): 1467–71. http://dx.doi.org/10.4028/www.scientific.net/amr.361-363.1467.

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The complex social-ecological systems network is an integrative platform of ecology, economy, management and complex networks which providing a new perspective on the comprehensive management of ecological and socio economical processes. Through research of the structures, functions and processes, one four-dimensional conceptual model of the complex social-ecological system for sustainable development was set up. The complex social-ecological systems comprise of natural subsystem, social subsystem, economic subsystem and integrative decision subsystem. The complex social-ecological systems network was defined as one six-element tuple which denotes the comprehensive spatial structure with different kinds of nodes of ecosystem, social system and economic system. The complex social-ecological systems network has some important characteristics including hierarchies, power-low, vulnerabilities, resilience, dynamics, co-evolution of flow and structure, et al. At last, based on the Multimedia Environment Pollutant Assessment System (MEPAS) of US EPA, the relationship between POPs (Persistent Organic Pollutants) exposure and lifetime fatal cancer risk was studied, and comprehensive risk network of the Taihu basin water pollution and human body health was established.

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22

Cruz Belmont, Cuahutli Alberto, and Pedro Humberto Moreno Salazar. "The social Protection network in Mexico: towards an inclusive social security system?" Denarius. Revista de economía y adminstración 2019, no. 38 (June 1, 2020): 71–102. http://dx.doi.org/10.24275/uam/izt/dcsh/denarius/v2020n38/cruz.

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23

Cheng, Wai Khuen, Wai Chun Leong, Joi San Tan, Zeng-Wei Hong, and Yen-Lin Chen. "Affective Recommender System for Pet Social Network." Sensors 22, no. 18 (September 7, 2022): 6759. http://dx.doi.org/10.3390/s22186759.

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In this new era, it is no longer impossible to create a smart home environment around the household. Moreover, users are not limited to humans but also include pets such as dogs. Dogs need long-term close companionship with their owners; however, owners may occasionally need to be away from home for extended periods of time and can only monitor their dogs’ behaviors through home security cameras. Some dogs are sensitive and may develop separation anxiety, which can lead to disruptive behavior. Therefore, a novel smart home solution with an affective recommendation module is proposed by developing: (1) an application to predict the behavior of dogs and, (2) a communication platform using smartphones to connect with dog friends from different households. To predict the dogs’ behaviors, the dog emotion recognition and dog barking recognition methods are performed. The ResNet model and the sequential model are implemented to recognize dog emotions and dog barks. The weighted average is proposed to combine the prediction value of dog emotion and dog bark to improve the prediction output. Subsequently, the prediction output is forwarded to a recommendation module to respond to the dogs’ conditions. On the other hand, the Real-Time Messaging Protocol (RTMP) server is implemented as a platform to contact a dog’s friends on a list to interact with each other. Various tests were carried out and the proposed weighted average led to an improvement in the prediction accuracy. Additionally, the proposed communication platform using basic smartphones has successfully established the connection between dog friends.

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24

M R, Archana. "Social Network based Question and Answer System." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (May 31, 2019): 2079–86. http://dx.doi.org/10.22214/ijraset.2019.5347.

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25

Bergthaler, Andreas, and Jörg Menche. "The immune system as a social network." Nature Immunology 18, no. 5 (May 2017): 481–82. http://dx.doi.org/10.1038/ni.3727.

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26

Madani, Youness, Mohammed Erritali, Jamaa Bengourram, and Francoise Sailhan. "Social Network Analysis." Journal of Information Technology Research 13, no. 3 (July 2020): 142–55. http://dx.doi.org/10.4018/jitr.2020070109.

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Sentiment analysis has become an important field in scientific research in recent years. The goal is to extract opinions and sentiments from written text using artificial intelligence algorithms. In this article, we propose a new approach for classifying Twitter data into classes (positive, negative, and neutral). The proposed method is based on two approaches, a dictionary-based approach using the sentimental dictionary SentiWordNet, and an approach based on the fuzzy logic system (fuzzification, rule inference, and defuzzification). Experimental results show that our approach outperforms some other approaches in the literature and that by using the fuzzy logic we improve the quality of the classification.

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27

Zhu, Zhi Yong, and Lin Feng Bai. "The Design of College Network System." Applied Mechanics and Materials 34-35 (October 2010): 1347–50. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1347.

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The concept of participating management in social management was consulted; we have proposed a new concept of user-participating the network management. The core of this concept is people network and people management. The new network management philosophy will fulfill to convert the unified management of traditional network from co-management; forming one type of everyone can use networks and everyone participate in the network management.

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28

Cotterell, John. "Adolescent Reliance on Social Networks in Career Exploration." Australian Journal of Career Development 6, no. 1 (April 1997): 27–31. http://dx.doi.org/10.1177/103841629700600109.

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This paper reviews research on the role of network ties in job-finding in order to suggest how social networks may operate as information-based systems exerting influences on adolescent career exploration. Strong network ties influence job-finding differently than weak ties. Reliance on network ties as information sources may explain the reluctance of some adolescents to seek advice from the school's formal career advisory system. Suggestions are given on how to provide a context for adolescent career exploration, using knowledge of network ties.

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29

Wang, Zhi Kun. "Application of Complex Network Theory in Computer Network Topology Optimization Research." Advanced Materials Research 989-994 (July 2014): 4237–40. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4237.

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If we apply the system internal elements as nodes, and the relationship between the elements as connection, then the system form a network. If we put emphasis on the structure of the system and analyze the function of the system from the angle of structure, we’ll find that real network topology properties differ from previous research network, and has numerous nodes, which is called complex networks. In the real word, many complex systems can be basically described by the network, while the reality is that complex systems can be called as “complex network”, such as social network, transportation network, power grids and internet etc. In recent years, many articles about the complex networks are released in the international first-class publications such as Nature, PRL, PNAS, which reflects that the complex networks has become a new research focus.

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Đuričanin, Jasminka, Marko Gašić, Jelena Veličković, and Nebojša Pavlović. "Advertising on Facebook social network." Bizinfo Blace 12, no. 2 (2021): 171–81. http://dx.doi.org/10.5937/bizinfo2102171d.

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The exponential growth of users on social networks around the world has led companies to explore effective ways of their presence on social networks. Accordingly, the trend of advertising as one of the most important forms of communication mix has changed and now companies are mainly focused on advertising on social networks. There are many social networks that companies can use for advertising, however, this paper points to the importance of advertising through the social network Facebook, given the fact that Facebook is the largest and most popular social network in the world and is the perfect marketing tool with a built-in advertising system, allows businesses to use each user's information for targeted advertising. Hence, Facebook is the most dominant social network for advertising, which every company should take into account when creating marketing strategies, in order to gain and maintain a competitive advantage and maximize business success. The paper presents secondary data that clearly indicate that Facebook is the social network that has the most potential for advertising, reaching and engaging consumers, which is supported by a discussion of the results of empirical research.

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Aligamat Dolkhanova, Kifayat. "Social networks today and their prospects." SCIENTIFIC WORK 60, no. 11 (November 6, 2020): 122–25. http://dx.doi.org/10.36719/2663-4619/60/122-125.

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The article describes scientific and pedagogical research, which shows the idea under discussion is a very effective idea. Currently existing control systems have been analyzed and opportunities for the development of information technology in modern computing technologies, the systematic use of software tools, as well as the possibility of the system functioning at a certain level have been identified. Our current perspective is with the rapid growth of human development in social networks, there is a need for analysis, such as networks of influence, taking into account the interaction of network members, the dynamics of ideas. Over the past decade, the development of information and telecommunication technologies, the importance of new types of resources, as well as online social networks has increased significantly as a means of disseminating ideas that affect the behavior of network users. Key words: innovative technology, social information network, ICT, social media, "Multimedia" Information Systems Technology Center, VPN technology, High Technology Park of ANAS Limited Liability Company

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32

Zhao, Rongmei, Xi Xiong, Xia Zu, Shenggen Ju, Zhongzhi Li, and Binyong Li. "A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks." Complexity 2020 (August 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/9071624.

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Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.

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Larrain, Nicolás, Sophie Wang, Tom Stargardt, and Oliver Groene. "Cooperation Improvement in an Integrated Healthcare Network: A Social Network Analysis." International Journal of Integrated Care 23 (June 26, 2023): 32. http://dx.doi.org/10.5334/ijic.6519.

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Background: Cooperation is a core feature of integrated healthcare systems and an important link in their value-creating mechanism. The premise is that providers who cooperate can promote more efficient use of health services while improving health outcomes. We studied the performance of an integrated healthcare system in improving regional cooperation. Methods: Using claims data and social network analysis, we constructed the professional network from 2004 to 2017. Cooperation was studied by analyzing the evolution of network properties at network and physician practice (node) level. The impact of the integrated system was studied with a dynamic panel model that compared practices that participated in the integrated system versus nonparticipants. Results: The regional network evolved favourably towards cooperation. Network density increased 1.4% on average per year, while mean distance decreased 0.78%. At the same time, practices participating in the integrated system became more cooperative compared to other practices in the region: Degree (1.64e-03, p = 0.07), eigenvector (3.27e-03, p = 0.06) and betweenness (4.56e-03, p < 0.001) centrality increased more for participating practices. Discussion: Findings can be explained by the holistic approach to patients’ care needs and coordination efforts of integrated healthcare. The paper provides a valuable design for performance assessment of professional cooperation. Highlights Using claims data and social network analysis, we identify a regional cooperation network and conduct a panel analysis to measure the impact of an integrated care initiative on enhancing professional cooperation. Physician practices participating in the integrated system became more cooperative and improved their influence in the regional network more than non-participating practices. Integrated healthcare systems effectively incentivize cooperation through a holistic approach to patient care needs and coordination efforts.

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A. Patil, Shalmali, and Reena Pagare. "Enhanced Hybrid Recommender System using Social Friend Network." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 10, no. 4 (November 4, 2014): 2023–31. http://dx.doi.org/10.24297/ijmit.v10i4.627.

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Lots of people employ recommender systems to diminish the information overload over the internet. This leads the user in a personalized manner to hit upon interesting or helpful objects in a huge space of possible options. Amongst different techniques, Collaborative filtering recommender system has pulled off great success. But this technique pays no heed towards the social relationship of the users. This problem gave birth to the Social recommender system technology which possesses the capability to recognize users likings and preferences and their social relationships. In this paper, we present novel method where we combine collaborative filtering recommender system with social friend network to use social relationships. For this, we have made use of data related to users which provides their interests as well as their social relationship. Our method helps to find the friends with dissimilar tastes and determine the close friends amongst direct friends of targeted user which has more similar tastes. This proposed approach resulted in more precise and realistic results than traditional system.

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Li, Hao, Adrià Salvador Palau, and Ajith Kumar Parlikad. "A social network of collaborating industrial assets." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 4 (August 2018): 389–400. http://dx.doi.org/10.1177/1748006x18754975.

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The IoT (Internet of Things) concept is being widely regarded as the fundamental tool of the next industrial revolution – Industry 4.0. As the value of data generated in social networks has been increasingly recognised, social media and the IoT have been integrated in areas such as product-design, traffic routing, etc. However, the potential of this integration in improving system-level performance in industrial environments has rarely been explored. This paper discusses the feasibility of improving system-level performance in industrial systems by integrating social networks into the IoT concept. We propose the concept of a social internet of industrial assets (SIoIA) which enables the collaboration between assets by sharing status data. We also identify the building blocks of SIoIA and characteristics of one of its important components – social assets. A sketch of the general architecture needed to enable a social network of collaborating industrial assets is proposed and two illustrative application examples are given.

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Brinkley, Catherine. "The Small World of the Alternative Food Network." Sustainability 10, no. 8 (August 17, 2018): 2921. http://dx.doi.org/10.3390/su10082921.

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This research offers the first use of graph theory mathematics in social network analysis to explore relationships built through an alternative food network. The local food system is visualized using geo-social data from 110 farms and 224 markets around Baltimore County, Maryland, with 699 connections between them. Network behavior is explored through policy document review and interviews. The findings revealed a small-world architecture, with system resiliency built-in by diversified marketing practices at central nodes. This robust network design helps to explain the long-term survival of local food systems despite the meteoric rise of global industrial food supply chains. Modern alternative food networks are an example of a movement that seeks to reorient economic power structures in response to a variety of food system-related issues not limited to consumer health but including environmental impacts. Uncovering the underlying network architecture of this sustainability-oriented social movement helps reveal how it weaves systemic change more broadly. The methods used in this study demonstrate how social values, social networks, markets, and governance systems embed to transform both physical landscapes and human bodies. Network actors crafted informal policy reports, which were directly incorporated in state and local official land-use and economic planning documents. Community governance over land-use policy suggests a powerful mechanism for further localizing food systems.

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Jazmin, Enriquez-Sanchez, Munoz-Rodriguez Manrrubio, J. Reyes Altamirano-Cardenas, and Gante Abraham Villegas-De. "Activation process analysis of the Localized Agri-food System using social networks." Agricultural Economics (Zemědělská ekonomika) 63, No. 3 (March 7, 2017): 121–35. http://dx.doi.org/10.17221/254/2015-agricecon.

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The objective of the work was to analyse the prevailing activation process of the Localized Agri-food System (LAS) by using social networks as a tool to value the pre-existing social capital. There were 27 producers of “Chiapas Cream Cheese” and the members of the formal cheese maker organization from the state of Chiapas, Mexico that were interviewed. By the means of cluster analysis and the graphic design of friendship, the kinship, the “compadrazgo” knowledge, the collaboration and cooperation networks, we concluded that the structural activation must transcend the formal creation of an organization. It is best to value and then mobilize the pre-existing social capital in a territory with a specific traditional know-how as a foundation to the structure and activation process of the LAS. Four actors were identified for their active participation in all analysed networks; these were the information diffusers and network structures. Weak links in the cheese maker organization favour the innovation adoption; whereas the strong links maintain the know-how.

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Ma, Lizhu, and Xin Zhang. "Hierarchical Social Network Analysis Using a Multi-Agent System." International Journal of Agent Technologies and Systems 5, no. 3 (July 2013): 14–32. http://dx.doi.org/10.4018/ijats.2013070102.

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The quality of K-12 education has been a major concern in the nation for years. School systems, just like many other social networks, appear to have a hierarchical structure. Understanding this structure could be the key to better evaluating student performance and improving school quality. Many studies have been focusing on detecting hierarchical structure by using hierarchical clustering algorithms. The authors design an interaction-based similarity measure to accomplish hierarchical clustering in order to detect hierarchical structures in social networks (e.g. school district networks). This method uses a multi-agent system, for it is based on agent interactions. With the network structure detected, they also built a model, which is based on the MAXQ algorithm, to decompose the funding policy task into subtasks and then evaluate these subtasks by using funding distribution policies from past years and looking for possible relationships between student performances and funding policies. For the experiment, the authors used real school data from Bexar county’s 15 school districts in Texas. The first result shows that their interaction-based method is able to generate meaningful clustering and dendrograms for social networks. Additionally the authors’ policy evaluation model is able to evaluate funding policies from the past three years in Bexar County and conclude that increasing funding does not necessarily have a positive impact on student performance and it is generally not the case that the more is spent, the better.

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DU, WEN-BO, XIAN-BIN CAO, HAO-RAN ZHENG, HONG ZHOU, and MAO-BIN HU. "EVOLUTIONARY GAMES IN MULTI-AGENT SYSTEMS OF WEIGHTED SOCIAL NETWORKS." International Journal of Modern Physics C 20, no. 05 (May 2009): 701–10. http://dx.doi.org/10.1142/s0129183109013923.

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Much empirical evidence has shown realistic networks are weighted. Compared with those on unweighted networks, the dynamics on weighted network often exhibit distinctly different phenomena. In this paper, we investigate the evolutionary game dynamics (prisoner's dilemma game and snowdrift game) on a weighted social network consisted of rational agents and focus on the evolution of cooperation in the system. Simulation results show that the cooperation level is strongly affected by the weighted nature of the network. Moreover, the variation of time series has also been investigated. Our work may be helpful in understanding the cooperative behavior in the social systems.

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S, Santhosh Kumar, Vishnu Vardhan S, Wasim Jaffar M, Sultan Saleem A, and Sharmasth Vali Y. "Social Communicative Extraction Analysis." International Research Journal of Multidisciplinary Technovation 2, no. 4 (September 26, 2020): 4–10. http://dx.doi.org/10.34256/irjmt2042.

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The distinguishing proof of online networking networks has as of late been of significant worry, since clients taking an interest in such networks can add to viral showcasing efforts. Right now center around clients' correspondence considering character as a key trademark for recognizing informative systems for example systems with high data streams. We portray the Twitter Personality based Communicative Communities Extraction (T-PCCE) framework that recognizes the most informative networks in a Twitter organize chart thinking about clients' character. We at that point grow existing methodologies as a part of client’s character extraction by collecting information that speak to a few parts of client conduct utilizing AI strategies. We utilize a current measured quality based network discovery calculation and we expand it by embeddings a post-preparing step that dispenses with diagram edges dependent on clients' character. The adequacy of our methodology is exhibited by testing the Twitter diagram and looking at the correspondence quality of the removed networks with and without considering the character factor. We characterize a few measurements to tally the quality of correspondence inside every network. Our algorithmic system and the resulting usage utilize the cloud foundation and utilize the MapReduce Programming Environment. Our outcomes show that the T-PCCE framework makes the most informative networks.

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Xu, Ying Ming, and Shu Juan Jin. "Application of Computer System in the Study of Sociology." Advanced Materials Research 926-930 (May 2014): 1680–83. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1680.

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With the development of information technology, more and more data about social to be collected. If we can analyze them effectively, it will help people to understand sociological understanding, promoting the development of social science. But the increasing amount of data and analysis to put forward a huge challenge. Now the social networks have already surpassed the processing ability of the original analysis means, must use a more effective tool to complete the analysis task. The computer as a way of helping people from massive data to find the potential useful knowledge tools, play an important role in many fields. Social network analysis, also known as link mining, refers to the handling of the relationship between social network data in the computer method. In this paper, the methods of computer and the social network analysis was introduced in this paper and the computer algorithms are summarized in the application of social network analysis.

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Du, Qing. "A Relationship-based Social Question and Answer System in Social Network." Journal of Information and Computational Science 12, no. 10 (July 1, 2015): 3783–98. http://dx.doi.org/10.12733/jics20106027.

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Shen, Haiying, Ze Li, and Kang Chen. "Social-P2P: An Online Social Network Based P2P File Sharing System." IEEE Transactions on Parallel and Distributed Systems 26, no. 10 (October 1, 2015): 2874–89. http://dx.doi.org/10.1109/tpds.2014.2359020.

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OKHAPKINA, E. P. "DYNAMIC SYSTEM OF FUNCTIONING OF SOCIAL NETWORK COMMUNITIES." News of the Kabardin-Balkar Scientific Center of RAS 2, no. 106 (2022): 41–71. http://dx.doi.org/10.35330/1991-6639-2022-2-106-41-71.

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Sirisha, M. Sai, and S. S. S. Usha Devi N. "A Secure Social Network Question and Answer System." International Journal of Scientific Research in Computer Science and Engineering 6, no. 5 (October 31, 2018): 30–35. http://dx.doi.org/10.26438/ijsrcse/v6i5.3035.

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Lee, Seok-Pil. "Personalized Contents Recommendation System Based on Social Network." Journal of Broadcast Engineering 18, no. 1 (January 30, 2013): 98–105. http://dx.doi.org/10.5909/jbe.2013.18.1.98.

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HORI, Yukio, and MASAKAZU Yonekura. "Analysis of social network in campus computer system." Joho Chishiki Gakkaishi 14, no. 2 (2004): 49–52. http://dx.doi.org/10.2964/jsik_kj00001039568.

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Chang, Wei Lun, and Yi Ping Lo. "A social network based group decision support system." International Journal of Mobile Communications 10, no. 1 (2012): 41. http://dx.doi.org/10.1504/ijmc.2012.044522.

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Kaur, Sandeep, and Mini Ahuja. "Genetic algorithm based efficient social network Recommender System." International Journal of Computer Trends and Technology 36, no. 4 (June 25, 2016): 219–24. http://dx.doi.org/10.14445/22312803/ijctt-v36p138.

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Subramaniyaswamy, V., R. Logesh, V. Vijayakumar, and V. Indragandhi. "Automated Message Filtering System in Online Social Network." Procedia Computer Science 50 (2015): 466–75. http://dx.doi.org/10.1016/j.procs.2015.04.016.

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