Name
Abstract | ||
|---|---|---|
It is an important challenge to detect an overlapping community and its evolving tendency in a complex network. To our best knowledge, there is no such an overlapping community detection method that exhibits high normalized mutual information (NMI) and
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-score, and can also predict an overlapping community’s future considering node evolution, activeness, and multiscaling. This paper presents a novel method based on node vitality, an extension of node fitness for modeling network evolution constrained by multiscaling and preferential attachment. First, according to a node’s dynamics such as link creation and destruction, we find node vitality by comparing consecutive network snapshots. Then, we combine it with the fitness function to obtain a new objective function. Next, by optimizing the objective function, we expand maximal cliques, reassign overlapping nodes, and find the overlapping community that matches not only the current network but also the future version of the network. Through experiments, we show that its NMI and
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-score exceed those of the state-of-the-art methods under diverse conditions of overlaps and connection densities. We also validate the effectiveness of node vitality for modeling a node’s evolution. Finally, we show how to detect an overlapping community in a real-world evolving network. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSMC.2017.2779138 | IEEE Transactions on Systems, Man, and Cybernetics |
Keywords | Field | DocType |
Optimization,Linear programming,Social network services,Cybernetics,Mutual information,Vehicle dynamics,Aggregates | Mathematical optimization,Computer science,Evolving networks,Fitness function,Theoretical computer science,Linear programming,Complex network,Mutual information,Snapshot (computer storage),Cybernetics,Preferential attachment | Journal |
Volume | Issue | ISSN |
49 | 9 | 2168-2216 |
Citations | PageRank | References |
8 | 0.47 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiujun Cheng | 1 | 89 | 8.12 |
| Xiao Wu | 2 | 8 | 0.47 |
| MengChu Zhou | 3 | 8989 | 534.94 |
| Shangce Gao | 4 | 486 | 45.41 |
| Zhenhua Huang | 5 | 53 | 7.12 |
| Cong Liu | 6 | 128 | 14.67 |