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Scalable Community Search over Large-scale Graphs based on Graph Transformer

Authors:

Xiangying WuAuthors Info & Claims

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1680 - 1690

https://doi.org/10.1145/3626772.3657771

Published: 11 July 2024 Publication History

Abstract

Given a graph G and a query node q, community search (CS) aims to find a structurally cohesive subgraph from G that contains q. CS is widely used in many real-world applications, such as online recommendation and expert finding. Recently, the rise of learning-based CS methods has garnered extensive research interests, showcasing the promising potential of neural solutions. However, there remains room for optimization: (1) They initialize node features via classical methods, e.g., one-hot, random, and position encoding, which may fall short in capturing valuable community cohesiveness-related features. (2) The reliance on GCN or GCN-like models poses challenges in scaling to large graphs. (3) Existing methods do not adapt well to dynamic graphs, often requiring retraining from scratch. To handle this, we present CSFormer, a scalable CS based on Graph Transformer. First, we present a novel l-hop neighborhood community vector based on n-order h-index to represent each node's community features, generating a sequence of feature vectors by varying the neighborhood scope l. Then, we build a Transformer backbone to learn a good graph embedding that carries rich community features, based on which we perform a prediction-filtering-based online CS to efficiently return a community of q. We extend CSFormer to dynamic graphs and various community models. Extensive experiments on seven real-world graphs show our solution's superiority on effectiveness, e.g., we attain an average improvement of 20.6% in F1-score compared to the latest competitors.

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Cited By

Wang MMa DFu QZong C(2024)Conditional Community Search Based on Weight InformationElectronics10.3390/electronics1321432113:21(4321)Online publication date: 4-Nov-2024
https://doi.org/10.3390/electronics13214321
Wang JWang KLin XZhang WZhang Y(2024)Efficient Unsupervised Community Search with Pre-Trained Graph TransformerProceedings of the VLDB Endowment10.14778/3665844.366585317:9(2227-2240)Online publication date: 6-Aug-2024
https://dl.acm.org/doi/10.14778/3665844.3665853

Index Terms

Scalable Community Search over Large-scale Graphs based on Graph Transformer
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models
  2. Information retrieval
    1. Information retrieval query processing

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cover image ACM Conferences

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2024

3164 pages

ISBN:9798400704314

DOI:10.1145/3626772

General Chairs:
Grace Hui Yang
Georgetown University, USA
,
Hongning Wang
Tsinghua University, China
,
Sam Han
The Washington Post, USA
,
Program Chairs:
Claudia Hauff
Spotify, Netherlands
,
Guido Zuccon
The University of Queensland, Australia
,
Yi Zhang
University of California Santa Cruz, USA

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Published: 11 July 2024

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July 14 - 18, 2024

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Cited By

Wang MMa DFu QZong C(2024)Conditional Community Search Based on Weight InformationElectronics10.3390/electronics1321432113:21(4321)Online publication date: 4-Nov-2024
https://doi.org/10.3390/electronics13214321
Wang JWang KLin XZhang WZhang Y(2024)Efficient Unsupervised Community Search with Pre-Trained Graph TransformerProceedings of the VLDB Endowment10.14778/3665844.366585317:9(2227-2240)Online publication date: 6-Aug-2024
https://dl.acm.org/doi/10.14778/3665844.3665853

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