Name
Abstract | ||
|---|---|---|
Given a graph $G$G and a vertex $q\in G$q∈G, the community search (CS) problem aims to efficiently find a subgraph of $G$G whose vertices are closely related to $q$q. Communities are prevalent in social and biological networks, and can be used in product advertisement and social event recommendation. In this paper, we study profiled community search (PCS), where CS is performed on a profiled graph. This is a graph in which each vertex has labels arranged in a hierarchical manner. Extensive experiments show that PCS can identify communities with themes that are common to their vertices, and is more effective than existing CS approaches. As a naive solution for PCS is highly expensive, we have also developed a tree index, which facilitates efficient and online solutions for PCS. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tkde.2018.2882837 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
Hardware,Information systems,Machine learning,Computer science,Collaboration,Software | Information system,Community search,Graph,Vertex (geometry),Biological network,Computer science,Theoretical computer science,Software,Artificial intelligence,Machine learning | Journal |
Volume | ISSN | Citations |
abs/1901.05451 | 1041-4347 | 1 |
PageRank | References | Authors |
0.35 | 24 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yankai Chen | 1 | 14 | 2.18 |
| Yixiang Fang | 2 | 227 | 23.06 |
| Reynold Cheng | 3 | 3069 | 154.13 |
| Yun Li | 4 | 443 | 53.24 |
| Xiaojun Chen | 5 | 1298 | 107.51 |
| Jie Zhang | 6 | 1995 | 156.26 |