Graph Transformers for ! ery Plan Representation: Potentials and Challenges
Chenghao Lyu, Guillaume Lachaud, G. Lozano et al.
· 0 citations
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Open access
Aug 2026
DyGADBench: A Comprehensive Benchmark for Anomaly Detection in Dynamic Graphs
An extensive empirical study of state-of-the-art DGAD models is conducted, revealing that detection difficulty increases consistently with anomaly complexity, from simple localized irregularities to coordinated and temporally persistent structures.
Mohamed Nazim Mezhoudi, Guillaume Lachaud, Yan-Lei Diao et al.
· Proceedings of the 32nd ACM... · 0 citations
Book
Open access
Aug 2026
DyGADBench: A Comprehensive Benchmark for Anomaly Detection in Dynamic Graphs
Dynamic graph anomaly detection (DGAD) is critical for a wide range of applications where abnormal events are rare, evolving, and tightly coupled with temporal context. Despite rapid progress in modeling dynamic graphs, the evaluation of DGAD methods remains fragmented, leaving the strengths, limitations, and trade-off...
Mohamed Nazim Mezhoudi, Guillaume Lachaud, Yanlei Diao et al.
· Proceedings of the 32nd ACM... · 0 citations