{"type":"ScholarlyArticle","title":"Exploiting k-Degree Locality to Improve Overlapping Community Detection","authors":[{"name":"Hongyi Zhang","slug":"hongyi-zhang","given":"Hongyi","family":"Zhang","affiliation":""},{"name":"Michael R. Lyu","slug":"michael-r-lyu","given":"Michael R.","family":"Lyu","affiliation":""},{"name":"Irwin King","slug":"irwin-king","given":"Irwin","family":"King","affiliation":""}],"abstract":"Community 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.","doi":null,"url":"https://ijcai.org/proceedings/2015/339","pdf":"https://ijcai.org/proceedings/2015/0339.pdf","markdown":"https://ijcai.org/proceedings/2015/339.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/339","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Web Mining","track":"Main Track","number":339,"pages":{"first":2394,"last":2399},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}