Truss-based Community Search over Large Directed Graphs

桁架 计算机科学 搜索引擎索引 群落结构 理论计算机科学 局部搜索(优化) 时间复杂性 无向图 数学优化 图形 算法 数学 人工智能 组合数学 结构工程 工程类
作者
Qing Liu,Minjun Zhao,Xin Huang,Jianliang Xu,Yunjun Gao
标识
DOI:10.1145/3318464.3380587
摘要

Community search enables personalized community discovery and has wide applications in large real-world graphs. While community search has been extensively studied for undirected graphs, the problem for directed graphs has received attention only recently. However, existing studies suffer from several drawbacks, e.g., the vertices with varied in-degrees and out-degrees cannot be included in a community at the same time. To address the limitations, in this paper, we systematically study the problem of community search over large directed graphs. We start by presenting a novel community model, called D-truss, based on two distinct types of directed triangles, i.e., flow triangle and cycle triangle. The D-truss model brings nice structural and computational properties and has many advantages in comparison with the existing models. With this new model, we then formulate the D-truss community search problem, which is proved to be NP-hard. In view of its hardness, we propose two efficient 2-approximation algorithms, named Global and Local, that run in polynomial time yet with quality guarantee. To further improve the efficiency of the algorithms, we devise an indexing method based on D-truss decomposition. Consequently, the D-truss community search can be solved upon the D-truss index without time-consuming accesses to the original graph. Experimental studies on real-world graphs with ground-truth communities validate the quality of the solutions we obtain and the efficiency of the proposed algorithms.
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