Name
Abstract | ||
|---|---|---|
Communities are prevalent in social networks, knowledge graphs, and biological networks. Recently, the topic of community search (CS) has received plenty of attention. Given a query vertex, CS looks for a dense subgraph that contains it. Existing CS solutions do not consider the spatial extent of a community. They can yield communities whose locations of vertices span large areas. In applications that facilitate the creation of social events (e.g., finding conference attendees to join a dinner), it is important to find groups of people who are physically close to each other. In this situation, it is desirable to have a spatial-aware community (or SAC), whose vertices are close structurally and spatially. Given a graph G and a query vertex q, we develop exact solutions for finding an SAC that contains q. Since these solutions cannot scale to large datasets, we have further designed three approximation algorithms to compute an SAC. We have performed an experimental evaluation for these solutions on both large real and synthetic datasets. Experimental results show that SAC is better than the communities returned by existing solutions. Moreover, our approximation solutions can find SACs accurately and efficiently. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.14778/3055330.3055337 | PVLDB |
Field | DocType | Volume |
Data mining,Community search,Approximation algorithm,Graph,Knowledge graph,Social network,Vertex (geometry),Biological network,Computer science,Theoretical computer science,Spatial extent,Database | Journal | 10 |
Issue | ISSN | Citations |
6 | 2150-8097 | 44 |
PageRank | References | Authors |
1.00 | 20 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yixiang Fang | 1 | 227 | 23.06 |
| Reynold Cheng | 2 | 3069 | 154.13 |
| Xiaodong Li | 3 | 55 | 3.89 |
| Siqiang Luo | 4 | 240 | 14.59 |
| Jiafeng Hu | 5 | 162 | 10.87 |