Communication Dans Un Congrès Année : 2024

Inductive Anomaly Detection in Dynamic Graphs with Accumulative Causal Walk Alignment

Résumé

Leveraging structural information and temporal evolution, dynamic graph-based methods have advanced anomaly detection, addressing challenges in real-world social and transaction networks. Despite progress, existing approaches, particularly those based on Graph Neural Networks, face challenges with long-range dependencies and encoding unseen nodes, affecting model performance and generalisation. This paper presents the Dual-Contextual Inductive Dynamic Graph Transformer (DCIDGT), an architecture that efficiently encodes both global and local contexts of target edges. Through a novel mechanism, Accumulative Causal Walk Alignment, DCIDGT captures global spatio-temporal information for unseen nodes and ensures their semantic alignment across snapshots. Our approach is evaluated on real-world cryptocurrency transaction datasets with PR AUC metric that significantly outperforms existing baselines, demonstrating its effectiveness and potential in dynamic graph representation learning.

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hal-05457730 , version 1 (14-01-2026)

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  • HAL Id : hal-05457730 , version 1

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Leshanshui Yang, Clément Chatelain, Sébastien Adam. Inductive Anomaly Detection in Dynamic Graphs with Accumulative Causal Walk Alignment. 21st International Workshop on Mining and Learning with Graphs, Sep 2024, Vilnius, Lithuania. ⟨hal-05457730⟩
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