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Abstract
Social networks are generally made of individuals who are linked by some types of interdependencies such as friendship. Most individuals in social networks have many linkages in terms of friends, connections, and/or followers. Among these linkages, some of them are stronger than others. For instance, some friends may be acquaintances of an individual, whereas others may be friends who care about him or her (e.g., who frequently post on his or her wall). In this study, we integrate data mining with social computing to form a social network mining algorithm, which helps the individual distinguish these strong friends from a large number of friends in a specific portion of the social networks in which he or she is interested. Moreover, our mining algorithm allows the individual to interactively change his or her mining parameters. Furthermore, we discuss applications of our social mining algorithm to organizational computing and e-commerce
Notes
1. 1This article is a revised and expanded version of our SCA 2011 paper (Cameron, Leung, and Tanbeer Citation2011). New materials include (i) techniques for handling interactive changes of the mining parameters (e.g., users’ interested portions of social networks) and (ii) discussions on mining of strong fans for e-commerce activities or organizational design and operations, as well as (iii) additional experimental results.
Additional information
Notes on contributors
Syed K. Tanbeer
Syed K. Tanbeer is a post-doctoral fellow in the Database and Data Mining Lab in the Department of Computer Science at the University of Manitoba, where his research interests include data mining, parallel and distributed mining, and knowledge discovery from social networks. Dr. Tanbeer received his Ph.D. in Computer Engineering from Kyung Hee University, South Korea. He holds a B.S. degree in Applied Physics and Electronics and an M.S. degree in Computer Science from the University of Dhaka, Bangladesh. He has worked as a faculty member in the Department of Computer Science and Information Technology at IUT-OIC, Bangladesh. Recent publications appear in Social Network Analysis and Mining as well as Expert Systems with Applications.
Carson K. Leung
Carson K. Leung is a full professor in the Department of Computer Science at the University of Manitoba. His research interests include the areas of databases, data mining, social media mining, and social network analysis, as well as social computing and its applications. His work has been published in the ACM Transactions on Database Systems, Social Network Analysis and Mining, Knowledge and Information Systems, IEEE Transactions on Industrial Electronics, IEEE ICDE, IEEE ICDM, and PAKDD, among others. He received the Best Paper Award at the 2012 International Conference on Social Computing and its Applications. Professor Leung has served as a Program Chair for the C3S2E 2009 and 2010 conferences, as an organizing committee member of ACM SIGMOD 2008, IEEE ICDM 2011, and IEEE/ACM ASONAM 2014, as well as a program committee member for numerous international conferences, including ACM KDD, ACM CIKM, and ECML/PKDD. He received his B.Sc.(Hons.), M.Sc., and Ph.D., all in computer science, from the University of British Columbia.
Juan J. Cameron
Juan J. Cameron is a graduate student, under the academic supervision of Professor Carson K. Leung, in the Department of Computer Science at the University of Manitoba. Mr. Cameron is interested in the research areas of data mining and knowledge discovery from social networks. He received his B.Sc. from Universidad Latina de Costa Rica. Recent publications appear in Social Network Analysis and Mining as well as CASoN.