Name
Title | ||
|---|---|---|
A Graph Clustering Algorithm Using Attraction-Force Similarity for Community Detection. |
Abstract | ||
|---|---|---|
Graph clustering is to partition a large graph into several subgraphs according to the topological structure and node characteristics of the graph. It can discover the community structures of complex networks and thus help researchers better understand the characteristics and structures of complex networks. This paper first proposes the concepts of direct attraction force and indirect attraction force. Then, it defines a new structural similarity, attraction-force similarity. Finally, the AF-Cluster algorithm is proposed based on the attraction-force similarity. Through the experimental analysis, we can conclude that the AF-Cluster algorithm is effective for clustering graph compared with other contrast algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2018.2889312 | IEEE ACCESS |
Keywords | Field | DocType |
Complex network,community discovery,graph clustering,similarity | Graph,Computer science,Algorithm,Structural similarity,Complex network,Attraction,Clustering coefficient,Cluster analysis,Partition (number theory) | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongfang Zhou | 1 | 19 | 4.83 |
| Bingyan Xi | 2 | 0 | 0.34 |
| Yihui Zhang | 3 | 0 | 0.34 |
| Junhuai Li | 4 | 39 | 16.44 |
| Facun Zhang | 5 | 0 | 0.68 |