Fast and Accurate Community Search Algorithm for Attributed Graphs

Matsugu, Shohei; Shiokawa, Hiroaki; Kitagawa, Hiroyuki

doi:10.1007/978-3-030-59003-1_16

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Regardless of indexing, they must compute many distances for complex networks at the querying time because the indexed subgraphs do not include many edges in the complex networks. Finally, the use of node attributes in complex networks has not been studied in existing works (Zhong et al 2015;Li et al 2019;Abeywickrama and Cheema 2017), although current networks (e.g., social networks) maintain various metadata for every node (Matsugu et al 2020;. Thus, a fast graph indexing algorithm for efficiently attributed k-NN queries remains elusive.…”

Section: Existing Approaches and Challengesmentioning

confidence: 99%

Indexing complex networks for fast attributed kNN queries

Kobayashi

Matsugu

Shiokawa

2022

Soc. Netw. Anal. Min.

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The k nearest neighbor (kNN) query is an essential graph data-management tool used for finding relevant data entities suited to a user-specified query node. Graph indexing methods have the potential to achieve a quick kNN search response and thus are promising approaches. However, they struggle to handle large-scale attributed complex networks. This is because constructing indices and querying kNN nodes in large-scale networks are computationally expensive, and they are not designed to handle node attributes included in the networks. In this paper, we propose a novel graph indexing algorithm, namely CT index, for fast kNN queries on large complex networks. To overcome the aforementioned limitations, our algorithm generates two types of indices based on the topological properties of complex networks. In addition, we further propose BAG index along with CT index so that our algorithm enables to explore kNN nodes based on the attribute similarity. Our extensive experiments on real-world graphs show that our algorithm achieves up to 18,074 times faster indexing and 146 times faster kNN query than other state-of-the-art methods.

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Section: Existing Approaches and Challengesmentioning

confidence: 99%

Indexing complex networks for fast attributed kNN queries

Kobayashi

Matsugu

Shiokawa

2022

Soc. Netw. Anal. Min.

Self Cite

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An Efficient Indexing Method for Dynamic Graph kNN

Matsugu,

Kobayashi,

Shiokawa

2024

Lecture Notes in Computer Science

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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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Fast and Accurate Community Search Algorithm for Attributed Graphs

Cited by 3 publications

References 14 publications

Indexing complex networks for fast attributed kNN queries

Indexing complex networks for fast attributed kNN queries

An Efficient Indexing Method for Dynamic Graph kNN

Fast Algorithm for Attributed Community Search

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