Name
Title | ||
|---|---|---|
Multi-objective Optimization with Nonnegative Matrix Factorization for Identifying Overlapping Communities in Networks. |
Abstract | ||
|---|---|---|
Community structure is one of the most important properties existing in complex networks, and community detection in complex networks is an intensively investigated problem in recent years. In real-world networks, a node is usually shared by several overlapping communities. The problem of detecting overlapping communities is much more complicated than the hard-partition problem. In this paper, a multi-objective immune algorithm with nonnegative matrix factorization as local search module (MOIA-Net) is proposed to uncover overlapping communities in networks. The proposed algorithm simultaneously optimizes two criteria, negative ratio association and ratio cut, to achieve a preferable soft-partition in networks. It adopts a nonnegative matrix factorization strategy as local search procedure to enhance the search ability. Experiments on synthetic networks show the efficiency of the proposed algorithm. |
Year | Venue | Field |
|---|---|---|
2016 | BIC-TA | Community structure,Mathematical optimization,Computer science,Multi-objective optimization,Non-negative matrix factorization,Complex network,Local search (optimization) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongmin Liu | 1 | 17 | 1.96 |
| Hao Li | 2 | 143 | 10.82 |
| Wei Zhao | 3 | 9 | 4.55 |