Gossip-based behavioral group identification in decentralized OSNs
2016 (English)In: 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, Springer, 2016, 676-691 p.Conference paper (Refereed)
DOSNs are distributed systems providing social networking services that become extremely popular in recent years. In DOSNs, the aim is to give the users control over their data and keeping data locally to enhance privacy. Therefore, identifying behavioral groups of users that share the same behavioral patterns in decentralized OSNs is challenging. In the fully distributed social graph, each user has only one feature vector and these vectors can not move to any central storage or other users in a raw form duo to privacy issues. We use a gossip learning approach where all users are involved with their local estimation of the clustering model and improve their estimations and finally converge to a final clustering model available for all users. In order to evaluate our approach, we implement our algorithm and test it in a real Facebook graph.
Place, publisher, year, edition, pages
Springer, 2016. 676-691 p.
Behavioral group identification, Decentralized Online Social Network (DOSN), Gossip learning, Newscast EM, Artificial intelligence, Cluster analysis, Data mining, Digital storage, Learning systems, Pattern recognition, Behavioral patterns, Decentralized OSNs, Distributed systems, Group identification, On-line social networks, Social networking services, Social networking (online)
IdentifiersURN: urn:nbn:se:kth:diva-195516DOI: 10.1007/978-3-319-41920-6_52ISI: 000386510300052ScopusID: 2-s2.0-84979017494ISBN: 9783319419190OAI: oai:DiVA.org:kth-195516DiVA: diva2:1049701
16 July 2016 through 21 July 2016
QC 201611252016-11-252016-11-032016-11-29Bibliographically approved