Paper Info
Title
Effective Community Search for Large Attributed Graphs.
Abstract
Given a graph G and a vertex q ∈ G, the community search query returns a subgraph of G that contains vertices related to q. Communities, which are prevalent in attributed graphs such as social networks and knowledge bases, can be used in emerging applications such as product advertisement and setting up of social events. In this paper, we investigate the attributed community query (or ACQ), which returns an attributed community (AC) for an attributed graph. The AC is a subgraph of G, which satisfies both structure cohesiveness (i.e., its vertices are tightly connected) and keyword cohesiveness (i.e., its vertices share common keywords). The AC enables a better understanding of how and why a community is formed (e.g., members of an AC have a common interest in music, because they all have the same keyword \"music\"). An AC can be \"personalized\"; for example, an ACQ user may specify that an AC returned should be related to some specific keywords like \"research\" and \"sports\". To enable efficient AC search, we develop the CL-tree index structure and three algorithms based on it. We evaluate our solutions on four large graphs, namely Flickr, DBLP, Tencent, and DBpedia. Our results show that ACs are more effective and efficient than existing community retrieval approaches. Moreover, an AC contains more precise and personalized information than that of existing community search and detection methods.
Year
DOI
Venue
2016
10.14778/2994509.2994538
PVLDB
Field
DocType
Volume
Data mining,Graph,Community search,Social network,Vertex (geometry),Computer science,Group cohesiveness,Database
Journal
9
Issue
ISSN
Citations 
12
2150-8097
54
PageRank 
References 
Authors
1.17
28
4
Name
Order
Citations
PageRank
Yixiang Fang122723.06
Reynold Cheng23069154.13
Siqiang Luo324014.59
Jiafeng Hu416210.87