Báo cáo viên: Đặng Tiến Đạt
Thời gian: 14h00 - 15h00, thứ 5 ngày 13 tháng 2 năm 2025
Địa điểm: Phòng 612, nhà A6, Viện Toán học
Tóm tắt: Modularity is a popular metric for evaluating community quality in the community detection on graphs. The first modularity function, introduced by Newman [1], is based on the principle that a strong community has a higher density of internal edges compared to external ones. Since then, various modularity functions have been developed for undirected, directed, and weighted graphs. In the case of bipartite graphs, Murata's [2] and Barber's [3] modularity functions represent key advancements, each reflecting a different definition of community structure in bipartite graphs. By leveraging stochastic processes, these two community definitions can be integrated with the modularity based on the random walk process [4].
Tài liệu
- Mark Newman and Michelle Girvan. Finding and evaluating community structure in networks. Physical review. E, Statistical, nonlinear, and soft matter physics, 69 2 Pt 2:026113, 2003.
- Tsuyoshi Murata. Detecting communities from bipartite networks based on bipartite modularities. 2009 International Conference on Computational Science and Engineering, 4:50–57, 2009.
- Michael J. Barber. Modularity and community detection in bipartite net- works. Physical review. E, Statistical, nonlinear, and soft matter physics, 76 6 Pt 2:066102, 2007.
- Tien Dat Dang, Duy Hieu Do, and Thi Ha Duong Phan. Community detection in directed graphs using stationary distribution and hitting times methods. Social Network Analysis and Mining, 13:1–30, 2023.
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