Open Access

ARTICLE

Sparse Structural Knowledge Enhanced Graph Neural Networks for Anomaly Detection in Social Networks

Zehan Li¹, Yingyi Li^2,*, Zhiwei Tang³, Xuemeng Zhai³, Jiandong Liang¹, Guangmin Hu³

1 School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China
2 College of Computer Science and Cyber Security (Pilot Software College), Chengdu University of Technology, Chengdu, China
3 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

* Corresponding Author: Yingyi Li. Email:

Computers, Materials & Continua 2026, 89(1), 86 https://doi.org/10.32604/cmc.2026.086118

Received 25 May 2026; Accepted 09 July 2026; Issue published 13 August 2026

Abstract

Social network platforms have become primary channels for information dissemination, yet they are increasingly exploited by anomalous users such as bots, fake accounts, and coordinated disinformation spreaders. These malicious actors manipulate public opinion, spread misinformation and undermine platform integrity, posing severe threats to the security of the online ecosystem. Accurate detection of such users is challenging because they often organize into sophisticated high-order connection patterns that extend beyond local neighborhoods. Existing methods address this by either injecting predefined motifs as handcrafted features, which lack flexibility to discover unknown patterns, or employing higher-order Graph neural networks (GNNs) at prohibitive costs. Crucially, neither method treats structural information as learnable knowledge that can be automatically acquired from data and explicitly represented. To bridge this gap, we propose SparseGNN, a structural-knowledge-enhanced framework for anomalous user detection. It regards atomic subgraph patterns as fundamental, learnable units of structural knowledge. This framework is concatenated with original node features and fed into any standard GNN, without modifying the backbone architecture. Experiments on real-world datasets demonstrate that SparseGNN improves the accuracy and F1-score of standard GNNs for anomalous users detection without requiring predefined patterns, while maintaining linear complexity. Because the learned atomic patterns capture global high-order topology, the resulting structural knowledge representation is inherently less sensitive to localized edge perturbations, incidentally conferring improved stability under adversarial structural attacks.

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Keywords

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