Articoli di riviste: "Online knowledge networks"

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Angiani, Giulio, Paolo Fornacciari, Eleonora Iotti, Monica Mordonini e Michele Tomaiuolo. "Participation in Online Social Networks". International Journal of Interactive Communication Systems and Technologies 8, n. 2 (luglio 2018): 36–55. http://dx.doi.org/10.4018/ijicst.2018070103.

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Why and how more and more people get involved and use social networking systems are critical topics in social network analysis (SNA). As a matter of fact, social networking systems bring online a growing number of acquaintances, for many different purposes. Both business interests and personal recreational goals are motivations for using online social networks (OSN) or other social networking systems. The participation in social networks is a phenomenon which has been studied with several theories, and SNA is useful for common business problems, e.g., launching distributed teams, retaining people with vital knowledge for the organization, improving access to knowledge and spreading ideas and innovation. Nevertheless, there are some difficulties, such as anti-social behaviors of participants, lack of incentives, organizational costs and risks. In this article, a survey of the basic features of SNA, participation theories and models are discussed, with emphasis on social capital, information spreading, motivations for participation, and anti-social behaviors of social network users.

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Melton, James, Robert Miller e Michelle Salmona. "Online Social Networks". International Journal of Information Systems and Social Change 3, n. 2 (aprile 2012): 24–38. http://dx.doi.org/10.4018/ijissc.2012040102.

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Previous research has shown that many college students in the United States post content to social networking sites that they know would be considered inappropriate by employers and other authority figures. However, the phenomenon has not been extensively studied in cross-cultural context. To address this knowledge gap, a survey of college students in Australia, Denmark, the United Kingdom, and the United States was conducted. The study found a universal tendency among the four groups: students knew the content they were posting would be considered inappropriate by employers and other authority figures, but they chose to post it anyway. The article also reports on differences in the way this tendency was manifested and on related aspects of social networking across cultures, including decisions about privacy and information disclosure.

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Li, Yongning, Lun Zhang e Ye Wu. "Understanding the Dynamics of Knowledge Building Process in Online Knowledge-Sharing Platform: A Structural Analysis of Zhihu Tag Network". Complexity 2022 (28 maggio 2022): 1–11. http://dx.doi.org/10.1155/2022/7392186.

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Through structural analysis of 8-year tag networks from online knowledge-sharing platforms, this study finds that, with the scale of tag networks growing quickly, the growth trend of number edges indicates that tag network follows densification law. The clustering coefficient and the average shortest path of the network show that the rapid growth of network size does not bring about the compartmentalization of the knowledge network, and the degree distribution of tag networks shows a truncated power-law distribution. According to the structural characteristics of the tag network, this study proposes a tag network model based on the BA model. Based on the preference attachment, the triadic closure mechanism is employed to construct the edges between the old nodes, which revises the limitation that the BA model only connects edges between old and new nodes. The results show that the simulation model matches the actual tag network structure well. The generation mechanism of the tag network model provides a reference for understanding the knowledge construction process of the online knowledge-sharing platform to a certain extent.

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Preusse, Julia, Jérôme Kunegis, Matthias Thimm, Steffen Staab e Thomas Gottron. "Structural Dynamics of Knowledge Networks". Proceedings of the International AAAI Conference on Web and Social Media 7, n. 1 (3 agosto 2021): 506–15. http://dx.doi.org/10.1609/icwsm.v7i1.14402.

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We investigate the structural patterns of the appearance and disappearance of links in dynamic knowledge networks. Human knowledge is nowadays increasingly created and curated online, in a collaborative and highly dynamic fashion. The knowledge thus created is interlinked in nature, and an important open task is to understand its temporal evolution. In this paper, we study the underlying mechanisms of changes in knowledge networks which are of structural nature, i.e., which are a direct result of a knowledge network's structure. Concretely, we ask whether the appearance and disappearance of interconnections between concepts (items of a knowledge base) can be predicted using information about the network formed by these interconnections. In contrast to related work on this problem, we take into account the disappearance of links in our study, to account for the fact that the evolution of collaborative knowledge bases includes a high proportion of removals and reverts. We perform an empirical study on the best-known and largest collaborative knowledge base, Wikipedia, and show that traditional indicators of structural change used in the link analysis literature can be classified into four classes, which we show to indicate growth, decay, stability and instability of links. We finally use these methods to identify the underlying reasons for individual additions and removals of knowledge links.

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Wu, Guile, e Shaogang Gong. "Peer Collaborative Learning for Online Knowledge Distillation". Proceedings of the AAAI Conference on Artificial Intelligence 35, n. 12 (18 maggio 2021): 10302–10. http://dx.doi.org/10.1609/aaai.v35i12.17234.

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Traditional knowledge distillation uses a two-stage training strategy to transfer knowledge from a high-capacity teacher model to a compact student model, which relies heavily on the pre-trained teacher. Recent online knowledge distillation alleviates this limitation by collaborative learning, mutual learning and online ensembling, following a one-stage end-to-end training fashion. However, collaborative learning and mutual learning fail to construct an online high-capacity teacher, whilst online ensembling ignores the collaboration among branches and its logit summation impedes the further optimisation of the ensemble teacher. In this work, we propose a novel Peer Collaborative Learning method for online knowledge distillation, which integrates online ensembling and network collaboration into a unified framework. Specifically, given a target network, we construct a multi-branch network for training, in which each branch is called a peer. We perform random augmentation multiple times on the inputs to peers and assemble feature representations outputted from peers with an additional classifier as the peer ensemble teacher. This helps to transfer knowledge from a high-capacity teacher to peers, and in turn further optimises the ensemble teacher. Meanwhile, we employ the temporal mean model of each peer as the peer mean teacher to collaboratively transfer knowledge among peers, which helps each peer to learn richer knowledge and facilitates to optimise a more stable model with better generalisation. Extensive experiments on CIFAR-10, CIFAR-100 and ImageNet show that the proposed method significantly improves the generalisation of various backbone networks and outperforms the state-of-the-art methods.

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Zhang GuoYin, e Li Heng. "Research on Semantic Networks Based Online Learning Knowledge Management". International Journal of Digital Content Technology and its Applications 6, n. 8 (31 maggio 2012): 126–34. http://dx.doi.org/10.4156/jdcta.vol6.issue8.15.

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Yang, Bo, Lulu Wang e Bayan Omar Mohammed. "Improving the organizational knowledge sharing through online social networks". Kybernetes 49, n. 11 (2 dicembre 2019): 2615–32. http://dx.doi.org/10.1108/k-07-2019-0508.

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Purpose Social technologies can offer a strong means for organizations to manage their information flows and thus make changes on the organizational knowledge sharing, which may then be linked to employees’ productivity and performance enhancements. The purpose of this paper is to predict the impact of using the online social network on employee motivation and employee motivation effects on organizational knowledge sharing. Design/methodology/approach From employees of tax affairs organization, data are collected. For evaluating the model’s elements, a questionnaire was designed. It was revised by experts with significant experiences. For statistical analysis, SMART-PLS 3.2 is used. Findings Findings have confirmed the validity of the proposed model. The results also have indicated that online social networks, social trust and social goals have a positive and important impact on employee motivation. Furthermore, obtained results have revealed that organizational knowledge sharing is significantly influenced by employee motivation and organizational commitment. Research limitations/implications The study contributes to the literature on organizational knowledge sharing and employee motivation in online social networking sites. Organizations could benefit from this knowledge by accepting that social networking sites must be considered as a critical component of the knowledge sharing, and precise targeting efforts could be directed for those users. Also, it could be exciting to study further factors affecting the development of organizational knowledge sharing in larger organizations. Originality/value The paper rises the understanding of what online social networking encompasses and how it can be utilized for the organization. The ideas and discussion are similarly applicable to libraries and may give them new visions into the delivery of social networking applications as part of their facilities to users.

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Mussina, A. B., S. S. Aubakirov e P. Trigo. "Architecture for enduring knowledge-extraction from online social networks". BULLETIN of the L N Gumilyov Eurasian National University MATHEMATICS COMPUTER SCIENCE MECHANICS Series 140, n. 3 (2022): 23–32. http://dx.doi.org/10.32523/2616-7182/bulmathenu.2022/3.3.

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Nowadays social networks and media play significant role in daily life. All our life in the real world is recorded in the digital space as well. Scientists have enormous potential in researching issues such as social influence ontop news and top news influence on society. Its impact on daily life spans such diverse areas as digital marketing, publicopinion analysis, political monitoring and disaster notification. Any task of processing such a large data stream needs acoherent architecture that will fit the analyzed resource. In the presented work, we set ourselves the task of creating ahighly loaded, fault-tolerant, scalable system for extracting and processing data from various social networks and analyzing data in real time. The solution is architecture in the form of a set of modules. Modules have their own characteristics depending on the work performed, from collecting textual data to direct processing and extraction of knowledge.

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Crowne, Kerri Anne, Richard J. Goeke e Mary Shoemaker. "Enhancing international assignees’ performance with online social networks". Journal of Global Mobility 3, n. 4 (14 dicembre 2015): 397–417. http://dx.doi.org/10.1108/jgm-09-2014-0045.

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Purpose – Adjustment to the new locale is one of the primary factors that influence performance on an international assignment. The purpose of this paper is to propose a model that depicts how online social networks may contribute to international assignees’ adjustment via their influence on well-being and knowledge transfer. Design/methodology/approach – The present research uses network theory and readily available technology to develop a model of how online supportive social networks and informational social networks may increase the international assignee’s well-being and knowledge transfer (with prior/next assignees and with the organization). These increases will subsequently have a positive impact on the assignee’s adjustment and ultimately his/her job performance. Findings – Since this paper is conceptual rather than empirical, there are no findings; however, it is argued that online social networks may have a positive impact on an expatriate’s well-being, knowledge transfer, adjustment, and job performance. Research limitations/implications – This paper is a conceptual piece, so data will need to be collected to test the model developed here. Furthermore, other factors may influence international assignee adjustment, such as spouse and/or family adjustment. Practical implications – Suggestions are provided regarding how organizations can utilize in-house or external online social networks to assist international assignees. Originality/value – Despite the ubiquity of online social networks, there is a paucity of research examining the potential impact of online social networks on international assignees.

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Gao, Liang, Xu Lan, Haibo Mi, Dawei Feng, Kele Xu e Yuxing Peng. "Multistructure-Based Collaborative Online Distillation". Entropy 21, n. 4 (2 aprile 2019): 357. http://dx.doi.org/10.3390/e21040357.

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Recently, deep learning has achieved state-of-the-art performance in more aspects than traditional shallow architecture-based machine-learning methods. However, in order to achieve higher accuracy, it is usually necessary to extend the network depth or ensemble the results of different neural networks. Increasing network depth or ensembling different networks increases the demand for memory resources and computing resources. This leads to difficulties in deploying depth-learning models in resource-constrained scenarios such as drones, mobile phones, and autonomous driving. Improving network performance without expanding the network scale has become a hot topic for research. In this paper, we propose a cross-architecture online-distillation approach to solve this problem by transmitting supplementary information on different networks. We use the ensemble method to aggregate networks of different structures, thus forming better teachers than traditional distillation methods. In addition, discontinuous distillation with progressively enhanced constraints is used to replace fixed distillation in order to reduce loss of information diversity in the distillation process. Our training method improves the distillation effect and achieves strong network-performance improvement. We used some popular models to validate the results. On the CIFAR100 dataset, AlexNet’s accuracy was improved by 5.94%, VGG by 2.88%, ResNet by 5.07%, and DenseNet by 1.28%. Extensive experiments were conducted to demonstrate the effectiveness of the proposed method. On the CIFAR10, CIFAR100, and ImageNet datasets, we observed significant improvements over traditional knowledge distillation.

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Palacios-Marqués, Daniel, Simona Popa e María Pilar Alguacil Mari. "The effect of online social networks and competency-based management on innovation capability". Journal of Knowledge Management 20, n. 3 (9 maggio 2016): 499–511. http://dx.doi.org/10.1108/jkm-05-2015-0175.

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Purpose The purpose of this paper is to explore the effect of online social networks and competency-based management on innovation capability. Design/methodology/approach The paper is theory-confirming. Theoretical relationships were tested using an empirical study of 289 firms from the Spanish biotechnology and telecommunications industries. Findings Results confirm that online social network use for internal cognitive processes (e.g. reading, searching and storing information) and external cognitive processes (e.g. sharing and co-creating knowledge) positively affects knowledge transfer. This knowledge helps firms to achieve superior competency in R&D to succeed in innovation programs. Research Limitations/implications All survey respondents were from Spain, which may limit the generalizability of findings. A longitudinal approach was not used. However, doing so would make it possible to explore time lags between online social network use, competency-based management and innovation. Practical Implications This paper highlights the potential as well as the limitations of online social networks and competency-based management in promoting innovation capability. Businesses must consciously manage the assimilation and use of online social networks to benefit from them. Originality/value The study contributes to the literature by identifying effects on innovation capability at the meso-level (i.e. online social networks). Findings highlight the need for a shift in focus away from collaborating and interacting in online social networks (micro-level) and organizational contexts (macro-level) so as to improve innovation capability.

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Haythornthwaite, Caroline. "Learning, connectivity and networks". Information and Learning Sciences 120, n. 1/2 (14 gennaio 2019): 19–38. http://dx.doi.org/10.1108/ils-06-2018-0052.

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PurposeThis is paper is concerned with the learning outcomes associated with connectivity through online networks, open online exchange and wider changes associated with contemporary information practices. The theme of connectivity is used here to capture both the detailed specificity of relations that define networks of learners and the ambient effect of wide accessibility to resources and people through open, online forums.Design/methodology/approachThe paper follows the idea of a network from the ground up, outlining the social network perspective as a way to consider the foundational bases of learning and networks, as well as the effect of ambient influence. The paper addresses the ways learning may be viewed as a social network relation, an interpersonal relationship and an outcome of interaction and connectivity, and how network connectivity can be used as input for design for learning.FindingsThe paper presents a range of perspectives and studies that view learning from a social network and connectivity perspective, emphasizing both the person-to-person connectivity of a learning tie and the impact of contemporary data and information sharing through the dynamics of open contributory practice.Practical implicationsThe outcome of connectivity in the service of learning is bound up with digital information practices, including individual practices of search, retrieval, participation, knowledge dissemination, knowledge construction and more. This paper provides a network perspective on learning relations that accommodates analysis in online and offline environments, but incorporates attention to the open, online retrieval and contributory practices that now influence learning practices and which may support design of new learning environments.Originality/valueThis paper offers insight into the way social networks and connectivity combine to show network relations, relationships, outcomes and design input at the actor, network and societal levels.

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Keary, Mae. "From Publishing to Knowledge Networks: Reinventing Online Knowledge Infrastructures20043Alexander Hars. From Publishing to Knowledge Networks: Reinventing Online Knowledge Infrastructures. Berlin: Springer Verlag 2003. 211 pp., ISBN: 3540012508 £54.00 (hard cover)". Online Information Review 28, n. 4 (agosto 2004): 313. http://dx.doi.org/10.1108/14684520410553831.

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Smith, Antoinette L., Ryan J. Baxter, Scott R. Boss e James E. Hunton. "The Dark Side of Online Knowledge Sharing (Retracted)". Journal of Information Systems 26, n. 2 (1 luglio 2012): 71–91. http://dx.doi.org/10.2308/isys-50244.

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ABSTRACT Given the growing trend of electronic networks of practice and the growing propensity of individuals to rely on the Internet for problem solving, we examine whether programmers in a hypothetical situation would be likely to disclose confidential information through an online forum in attempt to solve a programming problem. We hypothesize and find in a survey of 187 programmers that online forum commitment and trust lead to greater online forum participation, which in turn predicts a higher likelihood of confidential information disclosure. We also find that programmers with greater awareness of security policies exhibit a lower likelihood of deciding to risk disclosing confidential information. The study contributes to extant literature by raising and exploring the potentially negative, dark side of knowledge sharing through electronic networks of practice.

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Tao, Xiaohui, Haoran Xie, Yongrui (Louie) Qin, Xujuan Zhou e Yi Cai. "Knowledge discovery and management on online social networks and media". Web Intelligence 18, n. 3 (30 settembre 2020): 179–80. http://dx.doi.org/10.3233/web-200439.

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Moll, Ricarda, Stephanie Pieschl e Rainer Bromme. "Blessed Oblivion? Knowledge and Metacognitive Accuracy in Online Social Networks". International Journal of Developmental Science 9, n. 2 (3 agosto 2015): 57–60. http://dx.doi.org/10.3233/dev-14155.

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Canzian, Luca, e Mihaela Van Der Schaar. "Real-time stream mining: online knowledge extraction using classifier networks". IEEE Network 29, n. 5 (settembre 2015): 10–16. http://dx.doi.org/10.1109/mnet.2015.7293299.

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CHENG, Cheng, Chun-hong ZHANG e Yang JI. "Background knowledge based privacy metric model for online social networks". Journal of China Universities of Posts and Telecommunications 21, n. 2 (aprile 2014): 75–82. http://dx.doi.org/10.1016/s1005-8885(14)60289-2.

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Zhu, Hong-Miao, Sheng-Tai Zhang e Zhen Jin. "The effects of online social networks on tacit knowledge transmission". Physica A: Statistical Mechanics and its Applications 441 (gennaio 2016): 192–98. http://dx.doi.org/10.1016/j.physa.2015.08.044.

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Dai, Chenyun, Fang-Yu Rao, Traian Marius Truta e Elisa Bertino. "Privacy-Preserving Assessment of Social Network Data Trustworthiness". International Journal of Cooperative Information Systems 23, n. 02 (giugno 2014): 1441004. http://dx.doi.org/10.1142/s0218843014410044.

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Extracting useful knowledge from social network datasets is a challenging problem. While large online social networks such as Facebook and LinkedIn are well known and gather millions of users, small social networks are today becoming increasingly common. Many corporations already use existing social networks to connect to their customers. Seeing the increasing usage of small social networks, such companies will likely start to create in-house online social networks where they will own the data shared by customers. The trustworthiness of these online social networks is essentially important for decision making of those companies. In this paper, our goal is to assess the trustworthiness of local social network data by referencing external social networks. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.

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MALLAN, KERRY. "Space, Power and Knowledge: The Regulatory Fictions of Online Communities". International Research in Children's Literature 1, n. 1 (luglio 2008): 66–81. http://dx.doi.org/10.3366/e1755619808000112.

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This paper extends Donna Haraway's notion of ‘regulatory fictions’ to young people's participation in online communities. I argue that online communities produce a range of discursive practices and expectations, which attempt to constitute young people in particular ways. By combining recent empirical work on young people's participation in online communities with the representation of Internet culture in young adult literature, this paper examines how participation, both real and represented, involves young people in a negotiation of complex networks of space, power, and knowledge. The discussion highlights how these networks are shaped by regulatory practices, protocols, and politics. The paper posits that new technologies are contributing to the emergence of a new social paradigm, one that offers young people possibilities for construction of multiple identities and social networks. The empirical work derives from a current Australian Research Council grant. The primary texts examined are Destroying Avalon (2006) by Kate McCaffrey and ttyl (2004) by Lauren Myracle.

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Nguyen, Dang. "Network Life of Non-biomedical Knowledge". Journal of Digital Social Research 3, n. 2 (28 giugno 2021): 10–43. http://dx.doi.org/10.33621/jdsr.v3i2.82.

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Traditional medicine is hugely popular throughout Southeast Asia and other parts of the world. The development of the internet and online social networks in these contexts has enabled a significant proliferation of non-biomedical knowledge and practices via platforms such as Facebook. People use Facebook to advocate for non- biomedical alternatives to unaffordable biomedicine, share family medical recipes, discuss medicinal properties of indigenous plants, buy and sell these plants, and even crowdsource disease diagnoses. This paper examines the network characteristics of, and discourses present within, three popular Vietnamese non-biomedical knowledge Facebook sites over a period of five years. These large-scale datasets are studied using social network analysis and generative statistical models for topic analysis (Latent Dirichlet allocation). Forty-nine unique topics were quantitatively identified and qualitatively interpreted. Among these topics, themes of religion and philanthropy, critical discussions of traditional medicine, and negotiations involving overseas Vietnamese were particularly notable. Although non-biomedical networks on Facebook are growing both in terms of scale and popularity, sub-network comment activities within these networks exhibit ‘small world’ characteristics. This suggests that social media seem to be replicating existing social dynamics that historically enable the maintenance of traditional forms of medical knowledge, rather than transforming them here.

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Sher, Noa, e Sheizaf Rafaeli. "Associative linking for collaborative thinking: Self-organization of content in online Q&A communities via user-generated links". PLOS ONE 19, n. 3 (11 marzo 2024): e0300179. http://dx.doi.org/10.1371/journal.pone.0300179.

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Virtual collaborative Q&A communities generate shared knowledge through the interaction of people and content. This knowledge is often fragmented, and its value as a collective, collaboratively formed product, is largely overlooked. Inspired by work on individual mental semantic networks, the current study explores the networks formed by user-added associative links as reflecting an aspect of self-organization within the communities’ collaborative knowledge sharing. Using eight Q&A topic-centered discussions from the Stack Exchange platform, it investigated how associative links form internal structures within the networks. Network analysis tools were used to derive topological indicator metrics of complex structures from associatively-linked networks. Similar metrics extracted from 1000 simulated randomly linked networks of comparable sizes and growth patterns were used to generate estimated sampling distributions through bootstrap resampling, and 99% confidence intervals were constructed for each metric. The discussion-network indicators were compared against these. Results showed that participant-added associative links largely led to networks that were more clustered, integrated, and included posts with more connections than those that would be expected in random networks of similar size and growth pattern. The differences were observed to increase over time. Also, the largest connected subgraphs within the discussion networks were found to be modular. Limited qualitative observations have also pointed to the impacts of external content-related events on the network structures. The findings strengthen the notion that the networks emerging from associative link sharing resemble other information networks that are characterized by internal structures suggesting self-organization, laying the ground for further exploration of collaborative linking as a form of collective knowledge organization. It underscores the importance of recognizing and leveraging this latent mechanism in both theory and practice.

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Masud, Mehedi. "Knowledge Update in Collaborative Knowledge Sharing Systems". International Journal of Knowledge Society Research 6, n. 3 (luglio 2015): 19–31. http://dx.doi.org/10.4018/ijksr.2015070102.

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In a collaborative knowledge sharing system each source is associated with knowledge base system that participates in knowledge sharing with other sources. Acquaintances of sources build a collaborative knowledge sharing system or network in which each source is acquainted with other sources. The network of sources can be either acyclic or cyclic, meaning that they can contain acquaintance chains that are acyclic or cyclic. Updating knowledge in the sources involved in an acyclic logical network of sources is done by propagating an update from the originating source until the update reaches the leaves of the network. However, cyclic cases may create complexities due to conflicts that may arise between different versions of propagated updates. The author examines update propagation in both cyclic and acyclic networks. Moreover, the authors considers cases where a source is temporarily unavailable or offline. Here the author's propagation mechanism keeps track of every source even if the source is not available for a certain period of time until that source becomes available. Once a source comes back online the system must propagate the update destined to the returning sources to keep its knowledge consistent with other sources. The author has implemented this mechanism and evaluated it on a small collaborative knowledge sharing system.

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Xiao, Huilin, Weifeng Li, Xubin Cao e Zongming Tang. "The Online Social Networks on Knowledge Exchange: Online Social Identity, Social Tie and Culture Orientation". Journal of Global Information Technology Management 15, n. 2 (aprile 2012): 4–24. http://dx.doi.org/10.1080/1097198x.2012.11082753.

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Negm, Noha, e Hany Mahgoub. "Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques". International Journal of Computer Applications 178, n. 14 (15 maggio 2019): 12–21. http://dx.doi.org/10.5120/ijca2019918904.

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Bilgihan, Anil, Albert Barreda, Fevzi Okumus e Khaldoon Nusair. "Consumer perception of knowledge-sharing in travel-related Online Social Networks". Tourism Management 52 (febbraio 2016): 287–96. http://dx.doi.org/10.1016/j.tourman.2015.07.002.

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Subramani, Mani R., e Balaji Rajagopalan. "Knowledge-sharing and influence in online social networks via viral marketing". Communications of the ACM 46, n. 12 (dicembre 2003): 300–307. http://dx.doi.org/10.1145/953460.953514.

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Kasabov, N. "Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning". IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 31, n. 6 (2001): 902–18. http://dx.doi.org/10.1109/3477.969494.

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Mander, Sarah, Rebecca Cunningham, Louise Lever e Clair Gough. "Comparing Online and Offline Knowledge Networks of Carbon Capture and Storage". Energy Procedia 114 (luglio 2017): 7326–32. http://dx.doi.org/10.1016/j.egypro.2017.03.1863.

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Ye, Cuicui, Jing Yang e Yan Mao. "User identification for knowledge graph construction across multiple online social networks". Alexandria Engineering Journal 73 (luglio 2023): 145–58. http://dx.doi.org/10.1016/j.aej.2023.04.035.

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Zhang, Jingwen, e Damon Centola. "Social Networks and Health: New Developments in Diffusion, Online and Offline". Annual Review of Sociology 45, n. 1 (30 luglio 2019): 91–109. http://dx.doi.org/10.1146/annurev-soc-073117-041421.

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The relationship between social networks and health encompasses everything from the flow of pathogens and information to the diffusion of beliefs and behaviors. This review addresses the vast and multidisciplinary literature that studies social networks as a structural determinant of health. In particular, we report on the current state of knowledge on how social contagion dynamics influence individual and collective health outcomes. We pay specific attention to research that leverages large-scale online data and social network experiments to empirically identify three broad classes of contagion processes: pathogenic diffusion, informational and belief diffusion, and behavioral diffusion. We conclude by identifying the need for more research on ( a) how multiple contagions interact within the same social network, ( b) how online social networks impact offline health, and ( c) the effectiveness of social network interventions for improving population health.

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Mcfarland, Daniel, e Eric Klopfer. "Network Search: A New Way of Seeing the Education Knowledge Domain". Teachers College Record: The Voice of Scholarship in Education 112, n. 10 (ottobre 2010): 2664–702. http://dx.doi.org/10.1177/016146811011201004.

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Background The educational knowledge domain may be understood as a system composed of multiple, co-evolving networks that reflect the form and content of a cultural field. This paper describes the educational knowledge domain as having a community structure (form) based in relations of production (authoring) and consumption (referencing), and a cognitive structure (content) based in relations of ideas and concepts. Purpose We propose developing an online interactive system whereby the vast array of available knowledge artifacts can be mined for information reflective of these networks, and which can be visualized, measured, and explored over time. We argue that the creation of such a system will benefit the field of education manifold: it will greatly enhance individual learning of online materials; create more efficient searches; open access to knowledge creation and consumption to a wider public; enable greater practitioner involvement; facilitate direct study of the educational knowledge domain; and identify various means towards accelerated innovation. Design Building on the ideas of online communities, network visualizations, e-commerce, and advanced search engines, Scholar Practitioner Information Networks for Education (SPINE) not only facilitates access to education information resources, but also allows the community to view multiple sources of information in a relational context. At the heart of SPINE are multiple sources of information (journal articles, case studies, reviews, etc.), reviews of information, and ratings of reviewers. Connecting these sources are advanced analyses of production and consumption, allowing unique insights into established fields. Conclusion We illustrate that such a system is being developed in piecemeal fashion within other disciplines and propose a means by which they can be synthesized into a multi-network, interactive system for the field of education.

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Barker, Philip. "From Publishing to Knowledge Networks: Reinventing Online Knowledge Structures20046Alexander Hars. From Publishing to Knowledge Networks: Reinventing Online Knowledge Structures. Heidelberg: Springer‐Verlag 2003. 211 pp., ISBN: 3‐540‐01250‐8 US$79.95 (hard back)". Electronic Library 22, n. 3 (giugno 2004): 288–89. http://dx.doi.org/10.1108/02640470410541750.

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Zhang, Jingjing, Yehong Yang, Elena Barberà e Yu Lu. "Mapping Network Structure and Diversity of Interdisciplinary Knowledge in Recommended MOOC Offerings". International Review of Research in Open and Distributed Learning 23, n. 2 (1 maggio 2022): 1–24. http://dx.doi.org/10.19173/irrodl.v23i2.5590.

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In massive open online courses (MOOCs), recommendation relationships present a collection of associations that imply a new form of integration, such as an interdisciplinary synergy among diverse disciplines. This study took a computer science approach, using the susceptible-infected (SI) model to simulate the process of learners accessing courses within networks of MOOC offerings, and emphasized the potential effects of a network structure. The current low rate of access suggests that a ceiling effect influences learners’ access to learning online, given that there are thousands of courses freely available. Interdisciplinary networks were created by adding recommended courses into four disciplinary networks. The diversity of interdisciplinarity was measured by three attributes, namely variety, balance, and disparity. The results attest to interesting changes in how the diversity of interdisciplinary knowledge grows. Particularly remarkable is the degree to which the diversity of interdisciplinarity increased when new recommended courses were first added. However, changing diversity implied that neighbouring disciplines were more likely to come to the forefront to attach to the interdisciplinarity of MOOC offerings, and that the pace of synergy among disparate disciplines slowed as time passed. In the absence of domain experts, expert knowledge is not sufficient to support interdisciplinary curriculum design. More evidence-based analytics studies showing how interdisciplinarity evolves in course offerings could help us to better design online courses that prepare learners with 21st-century skills.

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Wu, Tianxing, Haofen Wang, Cheng Li, Guilin Qi, Xing Niu, Meng Wang, Lin Li e Chaomin Shi. "Knowledge graph construction from multiple online encyclopedias". World Wide Web 23, n. 5 (14 settembre 2019): 2671–98. http://dx.doi.org/10.1007/s11280-019-00719-4.

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Zhang, Yafei, Lin Wang, Jonathan J. H. Zhu, Xiaofan Wang e Alex ‘Sandy’ Pentland. "The Strength of Structural Diversity in Online Social Networks". Research 2021 (26 maggio 2021): 1–10. http://dx.doi.org/10.34133/2021/9831621.

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Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective outcomes. Leveraging a large-scale dataset from a knowledge-sharing website, this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one’s online social reputation. To capture the structural diversity of an individual, we first consider the number of weakly and strongly connected components in one’s contact neighborhood and further take the coexposure network of social neighbors into consideration. We show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation, and the inclusion of a coexposure network provides an additional ingredient to achieve that goal. After synthetically controlling several possible confounding factors through matching experiments, structural diversity still plays a nonnegligible role in the prediction of personal online social reputation. Our work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains, such as social influence and collective intelligence studies.

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Yang, Yuzhu. "On Detecting Abnormal Access for Online Ideological and Political Education". International Journal of Distributed Systems and Technologies 12, n. 2 (aprile 2021): 35–45. http://dx.doi.org/10.4018/ijdst.2021040104.

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With the development and spread of networks, online education has become a new way in education. The online education platform encounters a large number of concurrent visiting, while the system must guarantee network security in the process of online education. The network visiting requests are real-time and dynamic in online education. In order to detect network intrusion and abnormal access in real time and adapt to the dynamic changes of network visiting requests, this paper adopts a data stream-based network intrusion detection method to monitor and manage online education visiting. First, a knowledge library is constructed by normal visiting mode and abnormal visiting mode. Second, the dissimilarity between data point and data cluster is used to measure the similarity between normal mode and abnormal mode. Lastly, the knowledge library is updated to reflect the changes of network in online education system by re-clustering. The proposed method is evaluated on a real dataset.

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Liu, Shuo, Tao Gao e Tianze Li. "Design and Implementation of Large-Scale MOOC Platform in Colleges and Universities". International Journal of Advanced Pervasive and Ubiquitous Computing 9, n. 1 (gennaio 2017): 57–63. http://dx.doi.org/10.4018/ijapuc.2017010103.

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The large-scale online open course (MOOC) is a new online learning model in the field of distance open education in recent years. It embodies the learner as the center of the teaching philosophy, changing the traditional network curriculum content fixed, structured features, so that learners access to knowledge from simple linear to complex networks. For the MOOC learning management system framework, it is based on the existing LTSA model to increase the interaction agent and knowledge agent two components, so that the knowledge of the learning resource database can be updated with the progress of the curriculum. Learners can build a learning network through different ways to obtain knowledge.

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Cordero-Gutiérrez, Rebeca, e Libia Santos-Requejo. "Intention to participate in online commercial experiments by social network’s users". Management Research Review 39, n. 4 (18 aprile 2016): 378–98. http://dx.doi.org/10.1108/mrr-06-2014-0128.

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Purpose The purpose of this paper is to analyze the underlying differences in the intention to participate in online commercial experiments through the social network considering users’ gender and age. Design/methodology/approach The model of this paper uses two relevant variables, trust and attitude, to test the behavioral intention. There were 269 data sets from social network’s users. Factorial analysis and linear regression were used in the analysis of the data obtained to investigate the differences in gender and age in the intention to participate in online commercial experiments. Findings The results of this paper show that there exist differences among women and men, and among youthers and adults. Women and youthers are the most desirable groups to conduct commercial experiments through social networks. Research limitations/implications From the point of view of the academics, this paper increases the knowledge of social network’s possibilities as a marketing tool. Practical implications This study and its conclusions are relevant for entrepreneurs in any field who want to reach their customers through a horizontal social network because they can improve the online experiments’ profit. Entrepreneurs can know and understand their customers better, taking into account their wishes, tastes and interests through when participating in a commercial experiment. Originality/value This paper describes the possibilities that social networks like Facebook offer entrepreneurs to know the intention of users to participate in an online commercial experiment. Moreover, the differences in gender and age allow in adapting the contents of the online commercial experiments to get better results. In addition, this research contributes to the investigation in the possibilities of social networks as marketing tools.

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Shen, Chengchao, Xinchao Wang, Jie Song, Li Sun e Mingli Song. "Amalgamating Knowledge towards Comprehensive Classification". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 luglio 2019): 3068–75. http://dx.doi.org/10.1609/aaai.v33i01.33013068.

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With the rapid development of deep learning, there have been an unprecedentedly large number of trained deep network models available online. Reusing such trained models can significantly reduce the cost of training the new models from scratch, if not infeasible at all as the annotations used for the training original networks are often unavailable to public. We propose in this paper to study a new model-reusing task, which we term as knowledge amalgamation. Given multiple trained teacher networks, each of which specializes in a different classification problem, the goal of knowledge amalgamation is to learn a lightweight student model capable of handling the comprehensive classification. We assume no other annotations except the outputs from the teacher models are available, and thus focus on extracting and amalgamating knowledge from the multiple teachers. To this end, we propose a pilot two-step strategy to tackle the knowledge amalgamation task, by learning first the compact feature representations from teachers and then the network parameters in a layer-wise manner so as to build the student model. We apply this approach to four public datasets and obtain very encouraging results: even without any human annotation, the obtained student model is competent to handle the comprehensive classification task and in most cases outperforms the teachers in individual sub-tasks.

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Berezivska, Larysa D., Oleksandr P. Mikhno e Olha P. Pinchuk. "ONLINE PROJECTS AS A FORM OF SPREADING PEDAGOGICAL BIOGRAPHICAL KNOWLEDGE IN THE CONTEXT OF OPEN SCIENCE". Information Technologies and Learning Tools 97, n. 5 (30 ottobre 2023): 227–43. http://dx.doi.org/10.33407/itlt.v97i5.5240.

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The article deals with the relevant issue of using social media in the context of open science to disseminate the results of scientific research and implement the mission of institutions of social memory – libraries and museums – to preserve, study and popularize the national pedagogical heritage. It has been revealedthat in foreign and Ukrainian historiography there are no special studies on the spread of biographical knowledge in social networks. The online project is considered both as a multifunctional information resource and as a digital tool for educational activities, which, with the help of web technologies, demonstrates specially created open-access content to a wide range of users. For the first time, the paper analyzes the spread of biographical pedagogical knowledge through the development of online projects and their implementation by means of social networks. Biographical online projects of V. O. Sukhomlynskyi State Scientific and Educational Library of the National Academy of Educational Sciences of Ukraine and the Pedagogical Museum of Ukraine are characterized by the following parameters: systematized information in the form of a table (implementation model, name, web resource where the project was implemented, implementation period, hashtag), grouping by type (collective, individual, calendar, thematic), a brief description of the content of each project, a graphic layout of the project. The examples of biographical online projects have been given. The article reveals thetrend of an increase in the number of biographical online projects in the Ukrainian segment of the Facebook social network during 2020–2022, caused by the decolonization of the Ukrainian humanities. 4 proven models for implementing biographical online projects have been proposed for a widespread use: "Online project on Facebook", "Online project on Facebook and Instagram", Online project on Facebook using a web portal/website", Online project on Facebook as a source ofvirtual exhibition on the website". It has been proven that the use of social networks for the dissemination of pedagogical biography in the form of online projects in the educational and scientific space is not only an effective way of providing access to the results of scientific research to a wide audience, but it also contributes to the restoration and preservation of the national memory.

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Borza, Diana-Laura, Adrian Sergiu Darabant, Tudor Alexandru Ileni e Alexandru-Ion Marinescu. "Effective Online Knowledge Distillation via Attention-Based Model Ensembling". Mathematics 10, n. 22 (16 novembre 2022): 4285. http://dx.doi.org/10.3390/math10224285.

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Large-scale deep learning models have achieved impressive results on a variety of tasks; however, their deployment on edge or mobile devices is still a challenge due to the limited available memory and computational capability. Knowledge distillation is an effective model compression technique, which can boost the performance of a lightweight student network by transferring the knowledge from a more complex model or an ensemble of models. Due to its reduced size, this lightweight model is more suitable for deployment on edge devices. In this paper, we introduce an online knowledge distillation framework, which relies on an original attention mechanism to effectively combine the predictions of a cohort of lightweight (student) networks into a powerful ensemble, and use this as a distillation signal. The proposed aggregation strategy uses the predictions of the individual students as well as ground truth data to determine a set of weights needed for ensembling these predictions. This mechanism is solely used during system training. When testing or at inference time, a single, lightweight student is extracted and used. The extensive experiments we performed on several image classification benchmarks, both by training models from scratch (on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets) and using transfer learning (on Oxford Pets and Oxford Flowers datasets), showed that the proposed framework always leads to an improvement in the accuracy of knowledge-distilled students and demonstrates the effectiveness of the proposed solution. Moreover, in the case of ResNet architecture, we observed that the knowledge-distilled model achieves a higher accuracy than a deeper, individually trained ResNet model.

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Sasidharan, Sharath, Radhika Santhanam e Daniel Brass. "Assimilation of Enterprise Information Systems". International Journal of Technology Diffusion 8, n. 1 (gennaio 2017): 18–32. http://dx.doi.org/10.4018/ijtd.2017010102.

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Prior research has indicated that employees rely on their informal social network to acquire knowledge essential for assimilating new technologies into their work practices. This study investigates the role of the help desk and online help in providing knowledge support for individual users and workgroups after the implementation of an Enterprise Information System. In addition to the informal social network, the people-driven help desk and system-driven online help are incorporated into a multi-modal social network framework to understand their synergistic impact on implementation success. The findings of the study indicate that the help desk provides knowledge support to both individuals and workgroups, while online help has a nuanced effect that depends on the density of individual and workgroup social network connections. The study further emphasizes the relevance of multi-modal social networks in understanding the combined network effects of people and systems.

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Vătămănescu, Elena-Mădălina, Andreia Andrei, Patrizia Gazzola e Gandolfo Dominici. "Online Academic Networks as Knowledge Brokers: The Mediating Role of Organizational Support". Systems 6, n. 2 (14 aprile 2018): 11. http://dx.doi.org/10.3390/systems6020011.

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Cummings, Sarah, Richard Heeks e Marleen Huysman. "Knowledge and learning in online networks in development: a social-capital perspective". Development in Practice 16, n. 6 (novembre 2006): 570–86. http://dx.doi.org/10.1080/09614520600958215.

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Chang, Shuo, Wei Li, Yifan Zhang e Zhiyong Feng. "Online Siamese Network for Visual Object Tracking". Sensors 19, n. 8 (18 aprile 2019): 1858. http://dx.doi.org/10.3390/s19081858.

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Offline-trained Siamese networks are not robust to the environmental complication in visual object tracking. Without online learning, the Siamese network cannot learn from instance domain knowledge and adapt to appearance changes of targets. In this paper, a new lightweight Siamese network is proposed for feature extraction. To cope with the dynamics of targets and backgrounds, the weight in the proposed Siamese network is updated in an online manner during the tracking process. In order to enhance the discrimination capability, the cross-entropy loss is integrated into the contrastive loss. Inspired by the face verification algorithm DeepID2, the Bayesian verification model is applied for candidate selection. In general, visual object tracking can benefit from face verification algorithms. Numerical results suggest that the newly developed algorithm achieves comparable performance in public benchmarks.

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Jiang, Chong, Carolyn L. Beck e R. Srikant. "Bidding with limited statistical knowledge in online auctions". ACM SIGMETRICS Performance Evaluation Review 41, n. 4 (17 aprile 2014): 38–41. http://dx.doi.org/10.1145/2627534.2627546.

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Zhu, Yuqing, Jing Tang e Xueyan Tang. "Pricing influential nodes in online social networks". Proceedings of the VLDB Endowment 13, n. 10 (giugno 2020): 1614–27. http://dx.doi.org/10.14778/3401960.3401961.

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Influential nodes with rich connections in online social networks (OSNs) are of great values to initiate marketing campaigns. However, the potential influence spread that can be generated by these influential nodes is hidden behind the structures of OSNs, which are often held by OSN providers and unavailable to advertisers for privacy concerns. A social advertising model known as influencer marketing is to have OSN providers offer and price candidate nodes for advertisers to purchase for seeding marketing campaigns. In this setting, a reasonable price profile for the candidate nodes should effectively reflect the expected influence gain they can bring in a marketing campaign. In this paper, we study the problem of pricing the influential nodes based on their expected influence spread to help advertisers select the initiators of marketing campaigns without the knowledge of OSN structures. We design a function characterizing the divergence between the price and the expected influence of the initiator sets. We formulate the problem to minimize the divergence and derive an optimal price profile. An advanced algorithm is developed to estimate the price profile with accuracy guarantees. Experiments with real OSN datasets show that our pricing algorithm can significantly outperform other baselines.

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Parvandeh, Saeid, e Brett A. McKinney. "EpistasisRank and EpistasisKatz: interaction network centrality methods that integrate prior knowledge networks". Bioinformatics 35, n. 13 (27 novembre 2018): 2329–31. http://dx.doi.org/10.1093/bioinformatics/bty965.

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Abstract Motivation An important challenge in gene expression analysis is to improve hub gene selection to enrich for biological relevance or improve classification accuracy for a given phenotype. In order to incorporate phenotypic context into co-expression, we recently developed an epistasis-expression network centrality method that blends the importance of gene–gene interactions (epistasis) and main effects of genes. Further blending of prior knowledge from functional interactions has the potential to enrich for relevant genes and stabilize classification. Results We develop two new expression-epistasis centrality methods that incorporate interaction prior knowledge. The first extends our SNPrank (EpistasisRank) method by incorporating a gene-wise prior knowledge vector. This prior knowledge vector informs the centrality algorithm of the inclination of a gene to be involved in interactions by incorporating functional interaction information from the Integrative Multi-species Prediction database. The second method extends Katz centrality to expression-epistasis networks (EpistasisKatz), extends the Katz bias to be a gene-wise vector of main effects and extends the Katz attenuation constant prefactor to be a prior-knowledge vector for interactions. Using independent microarray studies of major depressive disorder, we find that including prior knowledge in network centrality feature selection stabilizes the training classification and reduces over-fitting. Availability and implementation Methods and examples provided at https://github.com/insilico/Rinbix and https://github.com/insilico/PriorKnowledgeEpistasisRank. Supplementary information Supplementary data are available at Bioinformatics online.

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