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Friend or Foe? Mining Suspicious Behavior via Graph Capsule Infomax Detector against Fraudsters

Published: 13 May 2024 Publication History

Abstract

Anomaly detection on graphs has recently attracted considerable attention due to its broad range of high-impact applications, including cybersecurity, financial transactions, and recommendation systems. Although many efforts have thus far been made, how to effectively handle the high inconsistency between users' behavior and labels, a fundamental issue in anomaly detection, has not yet received sufficient concern. Moreover, the inconsistency problem is hard to investigate and even deteriorates the performance of anomaly detectors. To this end, we propose a novel graph self-supervised learning framework, Capsule Graph Infomax (termed CapsGI), to overcome the inconsistency of anomaly detection. Inspired by the recent advances of capsules on images, we explore another possibility of reforming the node embedding by capsule ideas to represent the unique node's properties. Concretely, by disentangling heterogeneous factors underlying each node representation, we can establish node capsules such that their representation can reflect intrinsic node properties. To strengthen the connection among normal nodes, CapsGI further represents the part-whole contrastive learning between lower-level capsules (part) and higher-level capsules (whole) by explicitly considering the context graph relations. Extensive experiments on multiple real-world datasets demonstrate that our model significantly outperforms state-of-the-art models.

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References

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Cited By

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  • (2025)Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platformsExpert Systems with Applications10.1016/j.eswa.2024.125598262(125598)Online publication date: Mar-2025
  • (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
  • (2024)RMGANets: reinforcement learning-enhanced multi-relational attention graph-aware network for anti-money laundering detectionComplex & Intelligent Systems10.1007/s40747-024-01615-911:1Online publication date: 9-Nov-2024

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cover image ACM Conferences
WWW '24: Proceedings of the ACM Web Conference 2024
May 2024
4826 pages
ISBN:9798400701719
DOI:10.1145/3589334
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 13 May 2024

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Author Tags

  1. anomaly detection
  2. capsule network
  3. graph neural networks
  4. self-supervised learning

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WWW '24
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WWW '24: The ACM Web Conference 2024
May 13 - 17, 2024
Singapore, Singapore

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Cited By

View all
  • (2025)Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platformsExpert Systems with Applications10.1016/j.eswa.2024.125598262(125598)Online publication date: Mar-2025
  • (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
  • (2024)RMGANets: reinforcement learning-enhanced multi-relational attention graph-aware network for anti-money laundering detectionComplex & Intelligent Systems10.1007/s40747-024-01615-911:1Online publication date: 9-Nov-2024

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