Name
Abstract | ||
|---|---|---|
There is growing evidence that vertex similarity based on structural context is the basis of many link mining applications in complex networks. As a special case of vertex similarity, role similarity which measures the similarity between two vertices according to their roles in a network can facilitate the search for peer vertices. In RoleSim, graph automorphism is encapsulated into the role similarity measure. As a real-valued role similarity, RoleSim shows good interpretative power in experiments. However, RoleSim is not sufficient for some applications since it is very time-consuming and may assign unreasonable similarities in some cases. In this paper, we present CentSim, a novel role similarity metric which obeys all axiomatic properties for role similarity. CentSim can quickly calculate the role similarity between any two vertices by directly comparing their corresponding centralities. The experimental results demonstrate that CentSim achieves best performance in terms of efficiency and effectiveness compared with the state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-21042-1_10 | WEB-AGE INFORMATION MANAGEMENT (WAIM 2015) |
Keywords | Field | DocType |
Complex network, Vertex similarity, Role similarity, Vertex centrality, Similarity metric | Graph automorphism,Data mining,Vertex (geometry),Similarity measure,Computer science,Similarity (network science),Normalized compression distance,Centrality,Theoretical computer science,Complex network,Special case | Conference |
Volume | ISSN | Citations |
9098 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 16 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Longjie Li | 1 | 21 | 3.16 |
| Lvjian Qian | 2 | 6 | 0.90 |
| Victor E. Lee | 3 | 124 | 5.60 |
| mingwei leng | 4 | 5 | 0.79 |
| Mei Chen | 5 | 12 | 1.85 |
| Xiaoyun Chen | 6 | 60 | 10.21 |