We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Temporal community detection based on symmetric nonnegative matrix factorization.
- Authors
Jiao, Pengfei; Lyu, Haodong; Li, Xiaoming; Yu, Wei; Wang, Wenjun
- Abstract
To understand time-evolving networks, researchers should not only concentrate on the community structures, an essential property of complex networks, in each snapshot, but also study the internal evolution of the entire networks. Temporal communities provide insights into such mechanism, i.e., how the communities emerge, expand, shrink, merge, split and decay over time. Based on the symmetric nonnegative matrix factorization (SNMF), we present a dynamic model to detect temporal communities, which not only could find a well community structure in a given snapshot but also demands the results bear some similarity to the partition obtained from the previous snapshot. Moreover, our method can handle the situation that of the number of community changes in the networks. Also, a gradient descent algorithm is proposed to optimize the objective function of the model. Experimental results on both the synthetic and real-world networks indicate that our method outperforms the state-of-art methods for temporal community detection.
- Subjects
NONNEGATIVE matrices; FACTORIZATION; MATHEMATICAL analysis; ALGORITHMS; MATHEMATICAL functions
- Publication
International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics, 2017, Vol 31, Issue 13, p-1
- ISSN
0217-9792
- Publication type
Article
- DOI
10.1142/S0217979217501028