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Journal articles on the topic 'Online knowledge networks'

Author: Grafiati

Published: 27 July 2024

Last updated: 31 July 2025

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1

Melton, James, Robert Miller, and Michelle Salmona. "Online Social Networks." International Journal of Information Systems and Social Change 3, no. 2 (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 authorit

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Angiani, Giulio, Paolo Fornacciari, Eleonora Iotti, Monica Mordonini, and Michele Tomaiuolo. "Participation in Online Social Networks." International Journal of Interactive Communication Systems and Technologies 8, no. 2 (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 peo

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Smith, Antoinette L., Ryan J. Baxter, Scott R. Boss, and James E. Hunton. "The Dark Side of Online Knowledge Sharing (Retracted)." Journal of Information Systems 26, no. 2 (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 aw

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Zhang GuoYin, and Li Heng. "Research on Semantic Networks Based Online Learning Knowledge Management." International Journal of Digital Content Technology and its Applications 6, no. 8 (2012): 126–34. http://dx.doi.org/10.4156/jdcta.vol6.issue8.15.

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Yang, Bo, Lulu Wang, and Bayan Omar Mohammed. "Improving the organizational knowledge sharing through online social networks." Kybernetes 49, no. 11 (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 revise

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Mussina, A. B., S. S. Aubakirov, and 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, no. 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

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Preusse, Julia, Jérôme Kunegis, Matthias Thimm, Steffen Staab, and Thomas Gottron. "Structural Dynamics of Knowledge Networks." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 1 (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 in

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Guo, Zehan, Honghai Guan, Chungang He, Ye Xu, and Rui Liu. "Knowledge Graph-Enhanced Interleaved Multi-Head Attention Knowledge Tracing." International Journal of Data Warehousing and Mining 21, no. 1 (2025): 1–20. https://doi.org/10.4018/ijdwm.377619.

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The rise of online education demands improved learning assessment and personalization. Current knowledge tracing methods struggle with feature extraction, limited information interaction within learning data, and insufficient utilization of structured relationships between knowledge points. To address these challenges, this article proposes a knowledge graph-enhanced interleaved multi-head attention knowledge tracing model. The model integrates bidirectional long short-term memory networks, an interleaved multi-head attention mechanism, and graph convolutional networks into a deep learning fra

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MALLAN, KERRY. "Space, Power and Knowledge: The Regulatory Fictions of Online Communities." International Research in Children's Literature 1, no. 1 (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 discus

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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, no. 4 (2004): 313. http://dx.doi.org/10.1108/14684520410553831.

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Crowne, Kerri Anne, Richard J. Goeke, and Mary Shoemaker. "Enhancing international assignees’ performance with online social networks." Journal of Global Mobility 3, no. 4 (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 transfe

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Tao, Xiaohui, Haoran Xie, Yongrui (Louie) Qin, Xujuan Zhou, and Yi Cai. "Knowledge discovery and management on online social networks and media." Web Intelligence 18, no. 3 (2020): 179–80. http://dx.doi.org/10.3233/web-200439.

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Moll, Ricarda, Stephanie Pieschl, and Rainer Bromme. "Blessed Oblivion? Knowledge and Metacognitive Accuracy in Online Social Networks." International Journal of Developmental Science 9, no. 2 (2015): 57–60. http://dx.doi.org/10.3233/dev-14155.

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Canzian, Luca, and Mihaela Van Der Schaar. "Real-time stream mining: online knowledge extraction using classifier networks." IEEE Network 29, no. 5 (2015): 10–16. http://dx.doi.org/10.1109/mnet.2015.7293299.

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CHENG, Cheng, Chun-hong ZHANG, and Yang JI. "Background knowledge based privacy metric model for online social networks." Journal of China Universities of Posts and Telecommunications 21, no. 2 (2014): 75–82. http://dx.doi.org/10.1016/s1005-8885(14)60289-2.

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

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Liu, Ze, Zerui Shao, Haizhou Wang, and Beibei Li. "DDML: Multi-Student Knowledge Distillation for Hate Speech." Entropy 27, no. 4 (2025): 417. https://doi.org/10.3390/e27040417.

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Recent studies have shown that hate speech on social media negatively impacts users’ mental health and is a contributing factor to suicide attempts. On a broader scale, online hate speech can undermine social stability. With the continuous growth of the internet, the prevalence of online hate speech is rising, making its detection an urgent issue. Recent advances in natural language processing, particularly with transformer-based models, have shown significant promise in hate speech detection. However, these models come with a large number of parameters, leading to high computational requireme

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Sidorov, Michael, Ofer Hadar, and Dan Vilenchik. "Revisiting Information Cascades in Online Social Networks." Mathematics 13, no. 1 (2024): 77. https://doi.org/10.3390/math13010077.

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It is widely believed that a user’s activity pattern in Online Social Networks (OSNs) is strongly influenced by their friends or the users they follow. Building on this intuition, numerous models have been proposed over the years to predict information propagation in OSNs. Many of these models drew inspiration from the process of infectious spread within a population. While this approach is definitely plausible, it relies on knowledge of users’ social connections, which can be challenging to obtain due to privacy concerns. Moreover, while a significant body of work has focused on predicting ma

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Xiao, Huilin, Weifeng Li, Xubin Cao, and Zongming Tang. "The Online Social Networks on Knowledge Exchange: Online Social Identity, Social Tie and Culture Orientation." Journal of Global Information Technology Management 15, no. 2 (2012): 4–24. http://dx.doi.org/10.1080/1097198x.2012.11082753.

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Wan, Wen-Ning, Ting-Ju Wei, Tung-Huan Su, and Chuin-Shan Chen. "Decoding material networks: exploring performance of deep material network and interaction-based material networks." Journal of Mechanics 40 (2024): 796–807. https://doi.org/10.1093/jom/ufae053.

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ABSTRACT The deep material network (DMN) is a multiscale material modeling method well-known for its ability to extrapolate learned knowledge from elastic training data to nonlinear material behaviors. DMN is based on a two-layer building block structure. In contrast, the later proposed interaction-based material network (IMN) adopts a different approach, focusing on interactions within the material nodes rather than relying on laminate composite structures. Despite the increasing interest in both models, a comprehensive comparison of these two computational frameworks has yet to be conducted.

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Li, Yongning, Lun Zhang, and Ye Wu. "Understanding the Dynamics of Knowledge Building Process in Online Knowledge-Sharing Platform: A Structural Analysis of Zhihu Tag Network." Complexity 2022 (May 28, 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 pr

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Negm, Noha, and Hany Mahgoub. "Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques." International Journal of Computer Applications 178, no. 14 (2019): 12–21. http://dx.doi.org/10.5120/ijca2019918904.

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

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Subramani, Mani R., and Balaji Rajagopalan. "Knowledge-sharing and influence in online social networks via viral marketing." Communications of the ACM 46, no. 12 (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, no. 6 (2001): 902–18. http://dx.doi.org/10.1109/3477.969494.

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

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27

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

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Palacios-Marqués, Daniel, Simona Popa, and María Pilar Alguacil Mari. "The effect of online social networks and competency-based management on innovation capability." Journal of Knowledge Management 20, no. 3 (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. Thi

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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, no. 3 (2004): 288–89. http://dx.doi.org/10.1108/02640470410541750.

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Wu, Tianxing, Haofen Wang, Cheng Li, et al. "Knowledge graph construction from multiple online encyclopedias." World Wide Web 23, no. 5 (2019): 2671–98. http://dx.doi.org/10.1007/s11280-019-00719-4.

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Lee, Eun Jeong. "The Effects of Instagram Hashtags on Social Capital and Online Civic Engagement." International Journal of Interactive Communication Systems and Technologies 7, no. 1 (2017): 48–58. http://dx.doi.org/10.4018/ijicst.2017010104.

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This article applies social capital frameworks to examine the effects of Instagram hashtag use on heterogeneous and homogeneous networks as well as online civic engagement. Using an online survey of college students (N = 193), the results revealed that using Instagram hashtags for communication, connection, learning about people, and emotional support played an important role in the formation and strength of both heterogeneous and homogeneous networks. The study also found that heterogeneous networks are highly associated with informing current events and public affairs whereas homogeneous net

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Haythornthwaite, Caroline. "Learning, connectivity and networks." Information and Learning Sciences 120, no. 1/2 (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

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Wu, Guile, and Shaogang Gong. "Peer Collaborative Learning for Online Knowledge Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (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 op

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Vătămănescu, Elena-Mădălina, Andreia Andrei, Patrizia Gazzola, and Gandolfo Dominici. "Online Academic Networks as Knowledge Brokers: The Mediating Role of Organizational Support." Systems 6, no. 2 (2018): 11. http://dx.doi.org/10.3390/systems6020011.

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Cummings, Sarah, Richard Heeks, and Marleen Huysman. "Knowledge and learning in online networks in development: a social-capital perspective." Development in Practice 16, no. 6 (2006): 570–86. http://dx.doi.org/10.1080/09614520600958215.

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Zhu, Yuqing, Jing Tang, and Xueyan Tang. "Pricing influential nodes in online social networks." Proceedings of the VLDB Endowment 13, no. 10 (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 eff

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Anatolie, Tâțu. "Organizing Assessment in Distance Learning." Journal of Educational Theory and Practice DIDACTICA PRO... 22, no. 2-3 (132-133) (2022): 68–69. https://doi.org/10.5281/zenodo.6733889.

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Students and pupils can attend classes online thanks to the fact that most educational institutions have computers, local networks, and Internet access. Teachers can also use the virtual space to assess class performance and provide feedback on students’ progress. In recent years, educational institutions all over the world have explored the possibility of using telecommunications and computers to facilitate distance learning, or e-learning. Nowadays almost every educational institution has a history of online learning.   

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Parvandeh, Saeid, and Brett A. McKinney. "EpistasisRank and EpistasisKatz: interaction network centrality methods that integrate prior knowledge networks." Bioinformatics 35, no. 13 (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 express

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Dai, Yan, and Longbo Huang. "Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks." Proceedings of the ACM on Measurement and Analysis of Computing Systems 8, no. 3 (2024): 1–48. https://doi.org/10.1145/3700413.

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Stochastic Network Optimization (SNO) concerns scheduling in stochastic queueing systems and has been widely studied in network theory. Classical SNO algorithms require network conditions to be stationary w.r.t. time, which fails to capture the non-stationary components in increasingly many real-world scenarios. Moreover, most existing algorithms in network optimization assume perfect knowledge of network conditions before decision, which again rules out applications where unpredictability in network conditions presents. Motivated by these issues, this paper considers Adversarial Network Optim

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Dai, Yan, and Longbo Huang. "Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks." ACM SIGMETRICS Performance Evaluation Review 53, no. 1 (2025): 55–57. https://doi.org/10.1145/3744970.3727270.

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Stochastic Network Optimization (SNO) concerns scheduling in stochastic queueing systems and has been widely studied in network theory. Classical SNO algorithms require network conditions to be stationary w.r.t. time, which fails to capture the non-stationary components in increasingly many real-world scenarios. Moreover, most existing algorithms in network optimization assume perfect knowledge of network conditions before decision, which again rules out applications where unpredictability in network conditions presents. Motivated by these issues, this paper considers Adversarial Network Optim

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Dai, Chenyun, Fang-Yu Rao, Traian Marius Truta, and Elisa Bertino. "Privacy-Preserving Assessment of Social Network Data Trustworthiness." International Journal of Cooperative Information Systems 23, no. 02 (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

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Kreijns, Karel, Frederik Van Acker, Marjan Vermeulen, and Hans van Buuren. "Participation and Alienation in Online Networked Learning." Proceedings of the International Conference on Networked Learning 8 (April 2, 2012): 186–93. http://dx.doi.org/10.54337/nlc.v8.9088.

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The success of informal learning in large online learning networks largely depends on the existence of a sound social space that encompasses the networks of interpersonal relationships each individual has formed within this space. These personal social networks form the social capital of the individual and it is through this social capital that social interaction becomes possible and, thus, learning and knowledge co-creation can occur. It would appear that the existence of social capital is self-evident in large online learning networks as these networks are meant to connect people. However, o

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Jiang, Chong, Carolyn L. Beck, and R. Srikant. "Bidding with limited statistical knowledge in online auctions." ACM SIGMETRICS Performance Evaluation Review 41, no. 4 (2014): 38–41. http://dx.doi.org/10.1145/2627534.2627546.

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Palacios-Marqués, Daniel, José M. Merigó, and Pedro Soto-Acosta. "Online social networks as an enabler of innovation in organizations." Management Decision 53, no. 9 (2015): 1906–20. http://dx.doi.org/10.1108/md-06-2014-0406.

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Purpose – The purpose of this paper is to study the effect of online social networks on firm performance and how this technology can help to create value. The authors approach the problem from the Resource-Based View in order to analyze if online social networks can be considered source of competitive advantage and how it can enhance or complement essential marketing competences. Design/methodology/approach – The data were obtained from a survey based on the Spanish hospitality firms. This sector was chosen because Web 2.0 is becoming an important marketing channel in the tourism industry, and

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Nemec Zlatolas, Welzer, Hölbl, Heričko, and Kamišalić. "A Model of Perception of Privacy, Trust, and Self-Disclosure on Online Social Networks." Entropy 21, no. 8 (2019): 772. http://dx.doi.org/10.3390/e21080772.

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Online Social Networks are used widely, raising new issues in terms of privacy, trust, and self-disclosure. For a better understanding of these issues for Facebook users, a model was built that includes privacy value, privacy risk, trust, privacy control, privacy concerns, and self-disclosure. A total of 602 respondents participated in an online survey, and structural equation modeling was used to evaluate the model. The findings indicate significant relationships between the constructs in this study. The model from our study contributes new knowledge to privacy issues, trust and self-disclosu

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Gao, Liang, Xu Lan, Haibo Mi, Dawei Feng, Kele Xu, and Yuxing Peng. "Multistructure-Based Collaborative Online Distillation." Entropy 21, no. 4 (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 pe

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Liana, Tri Melda Mei, and John C. Walsh. "IMPACT OF KNOWLEDGE, SKILLS, AND WORK EXPERIENCE ON SUCCESS IN INDONESIA'S ONLINE AFFILIATE MARKETING." AMERICAN JOURNAL OF INTERDISCIPLINARY INNOVATIONS AND RESEARCH 6, no. 8 (2024): 1–9. http://dx.doi.org/10.37547/tajiir/nicspeep-2024-01-01.

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The purpose of this research is to find out and explain the variables of entrepreneurial knowledge, entrepreneurial abilities and work experience affect the variables of increasing business success in the Online Marketing Affiliate through the moderating variable of business performance (business) in the Online Marketing Affiliate. In this study, the independent variables are related to the things that must be owned by an entrepreneur, through the independent variables, namely entrepreneurial knowledge, entrepreneurial abilities and work experience, while the dependent variable is increasing b

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Zhang, Jingjing, Yehong Yang, Elena Barberà, and Yu Lu. "Mapping Network Structure and Diversity of Interdisciplinary Knowledge in Recommended MOOC Offerings." International Review of Research in Open and Distributed Learning 23, no. 2 (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 fre

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Qarkaxhija, Jusuf, Blerta Prevalla, and Shpëtim Latifi. "Knowledge and Perceptions About Mobile E-Commerce Technologies in Kosovo." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 23 (2022): 138–47. http://dx.doi.org/10.3991/ijim.v16i23.36211.

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Abstract - The enormous increase of online shopping, sales and online orders is disproportionate to the knowledge of the users of these services. The purpose of this paper is to understand how online shopping and orders are perceived by people who are directly or indirectly involved in online e commerce services. The study utilized questionnaires in the collection of data. Data was analyzed using Excel and percentages. Based on the results of the conducted study, it was revealed that very few people know how to distinguish between online shopping and online orders. The same hypothesis can be u

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Sher, Noa, and Sheizaf Rafaeli. "Associative linking for collaborative thinking: Self-organization of content in online Q&A communities via user-generated links." PLOS ONE 19, no. 3 (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 i

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