Flexible Community Search Algorithm on Attributed Graphs

Abstract

How can the most appropriate community be found given an attributed graph and a user-specified query node? The community search algorithm is currently an essential graph data management tool to find a community suited to a user-specified query node. Although community search algorithms are useful in various web-based applications and services, they have trouble handling attributed graphs due to the strict topological constraints of traditional algorithms. In this paper, we propose an accurate community search algorithm for attributed graphs. To overcome current limitations, we define a new attribute-driven community search problem class called the Flexible Attributed Truss Community (F-ATC). The advantage of the F-ATC problem is that it relaxes topological constraints, allowing diverse communities to be explored. Consequently, the community search accuracy is enhanced compared to traditional community search algorithms. Additionally, we present a novel heuristic algorithm to solve the F-ATC problem. This effective algorithm detects more accurate communities from attributed graphs than the traditional algorithms. Finally, extensive experiments are conducted using real-world attributed graphs to demonstrate that our approach achieves a higher accuracy than the state-of-the-art method.