Name
Abstract | ||
|---|---|---|
Given a graph G and a vertex q ∊ G, the community search (CS) problem aims to efficiently find a subgraph of G whose vertices are closely related to 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. Compared with existing CS approaches, PCS can sufficiently identify vertices with semantic commonalities and thus find more high-quality diverse communities. As a naive solution for PCS is highly expensive, we have developed a tree index, which facilitates efficient and online solutions for PCS. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDE.2019.00274 | 2019 IEEE 35th International Conference on Data Engineering (ICDE) |
Keywords | Field | DocType |
Indexes,Artificial intelligence,Collaboration,Flickr,Search problems,Biology,Semantics | Community search,Graph,Data mining,Vertex (geometry),Biological network,Computer science,Theoretical computer science,Semantics | Conference |
ISSN | ISBN | Citations |
1084-4627 | 978-1-5386-7474-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 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 |