short-paper

Graph Capsule Network with a Dual Adaptive Mechanism

Authors:

Xiangping Zheng,

Xuan ZhangAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1859 - 1864

https://doi.org/10.1145/3477495.3531764

Published: 07 July 2022 Publication History

Abstract

While Graph Convolutional Networks (GCNs) have been extended to various fields of artificial intelligence with their powerful representation capabilities, recent studies have revealed that their ability to capture the part-whole structure of the graph is limited. Furthermore, though many GCNs variants have been proposed and obtained state-of-the-art results, they face the situation that much early information may be lost during the graph convolution step. To this end, we innovatively present an Graph Capsule Network with a Dual Adaptive Mechanism (DA-GCN) to tackle the above challenges. Specifically, this powerful mechanism is a dual-adaptive mechanism to capture the part-whole structure of the graph. One is an adaptive node interaction module to explore the potential relationship between interactive nodes. The other is an adaptive attention-based graph dynamic routing to select appropriate graph capsules, so that only favorable graph capsules are gathered and redundant graph capsules are restrained for better capturing the whole structure between graphs. Experiments demonstrate that our proposed algorithm has achieved the most advanced or competitive results on all datasets.

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MP4 File (SIGIR22-sp1250.mp4)

This work proposes a novel graph capsule network with a dual adaptive mechanism (DA-GCN) to tackle the potential loss of relevant node information caused by graph convolution operations.

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Cited By

Zheng XHao XWu BBao XZhang XLi WLiang XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)A Sample-driven Selection Framework: Towards Graph Contrastive Networks with Reinforcement LearningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680644(10755-10764)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680644
Zheng XWu BLiang XLi WChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Friend or Foe? Mining Suspicious Behavior via Graph Capsule Infomax Detector against FraudstersProceedings of the ACM Web Conference 202410.1145/3589334.3645706(2684-2693)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645706

Index Terms

Graph Capsule Network with a Dual Adaptive Mechanism
1. Computing methodologies
  1. Artificial intelligence
2. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

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RMIT University
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Waseda University

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Published: 07 July 2022

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National Natural Science Foundation of China
National Social Science Foundation of China

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SIGIR '22

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SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Zheng XHao XWu BBao XZhang XLi WLiang XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)A Sample-driven Selection Framework: Towards Graph Contrastive Networks with Reinforcement LearningProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680644(10755-10764)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680644
Zheng XWu BLiang XLi WChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Friend or Foe? Mining Suspicious Behavior via Graph Capsule Infomax Detector against FraudstersProceedings of the ACM Web Conference 202410.1145/3589334.3645706(2684-2693)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645706

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