Exploiting k-Degree locality to improve overlapping community detection
Refereed conference paper presented and published in conference proceedings

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AbstractCommunity detection is of crucial importance in understanding structures of complex networks. In many real-world networks, communities naturally overlap since a node usually has multiple community memberships. One popular technique to cope with overlapping community detection is Matrix Factorization (MF). However, existing MF-based models have ignored the fact that besides neighbors, "local non-neighbors" (e.g., my friend's friend but not my direct friend) are helpful when discovering communities. In this paper, we propose a Locality-based Non-negative Matrix Factorization (LNMF) model to refine a preference-based model by incorporating locality into learning objective. We define a subgraph called "k-degree local network" to set a boundary between local non-neighbors and other non-neighbors. By discriminately treating these two class of non-neighbors, our model is able to capture the process of community formation. We propose a fast sampling strategy within the stochastic gradient descent based learning algorithm. We compare our LNMF model with several baseline methods on various real-world networks, including large ones with ground-truth communities. Results show that our model outperforms state-of-the-art approaches.
All Author(s) ListZhang H., Lyu M.R., King I.
Name of Conference24th International Joint Conference on Artificial Intelligence, IJCAI 2015
Start Date of Conference25/07/2015
End Date of Conference31/07/2015
Place of ConferenceBuenos Aires
Country/Region of ConferenceArgentina
Volume Number2015-January
Pages2394 - 2400
LanguagesEnglish-United Kingdom

Last updated on 2020-05-08 at 03:00