Name
Abstract | ||
|---|---|---|
Community detection is a significant but challenging task in the field of social network analysis. Many effective approaches have been proposed to solve this issue. However, most of them are mainly based on the topological structure or node features. In this study, we consider both these two aspects to detect non-overlapping and overlapping communities. Specifically, we define a novel quality metric based on closed topology and feature triangles. When this metric is used as an objective function, we propose a local learning framework to optimize it to achieve different community detection tasks. Extensive experiments on real-world social networks demonstrate that our framework achieves satisfactory results compared with other baseline approaches. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-06788-4_24 | Artificial Intelligence and Security |
Keywords | DocType | ISSN |
Community detection, Attributed network, Optimization, Social network analysis | Conference | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gao Guangliang | 1 | 0 | 0.34 |
| Sun Aiqin | 2 | 0 | 0.34 |
| Gu Haiyan | 3 | 0 | 0.34 |