Flexible Community Search Algorithm on Attributed Graphs

Matsugu, Shohei; Shiokawa, Hiroaki; Kitagawa, Hiroyuki

doi:10.1145/3366030.3366049

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…In our previous method [9], [10], we presented an accurate algorithm design compared to LocATC, however it consumes large computation time. In this paper, we extend [9], [10] to propose more efficient search methods based on [9], [10]. We also propose a preprocessing method for faster query response.…”

Section: Our Approaches and Contributionsmentioning

confidence: 99%

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Fast Algorithm for Attributed Community Search

Matsugu

Shiokawa

Kitagawa

2021

Journal of Information Processing

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Searching communities on attributed graphs has attracted much attention in recent years. 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 relax the topological constraints, we proposed a new model of the community. And we defined the problem of finding them in an attributed graph class called the Flexible Attributed Truss Community (F-ATC). The F-ATC problem has the advantage of being applicable in many situations because it can explore diverse communities. 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. For further optimization, we pre-processed the query response to make it faster. Finally, we conducted extensive experiments with real-world attributed graphs to demonstrate that our approach outperforms state-of-the-art methods.

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Section: Our Approaches and Contributionsmentioning

confidence: 99%

“…We refine [9] as our baseline algorithm, which is an algorithm to improve the accuracy of LocATC [3]. The baseline algorithm is based on the well-known beam search technique [12].…”

Section: Baseline Algorithmmentioning

confidence: 99%