GLADformer: A Mixed Perspective for Graph-level Anomaly Detection Article Swipe
Fan Xu
,
Nan Wang
,
Hao Wu
,
Xuezhi Wen
,
Dalin Zhang
,
Siyang Lu
,
Binyong Li
,
Wei Gong
,
Hai Wan
,
Xibin Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.00734
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.00734
Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs. Moreover, most contemporary methods are based on spatial domain and lack exploration of spectral characteristics. In this paper, we propose a multi-perspective hybrid graph-level anomaly detector namely GLADformer, consisting of two key modules. Specifically, we first design a Graph Transformer module with global spectrum enhancement, which ensures balanced and resilient parameter distributions by fusing global features and spectral distribution characteristics. Furthermore, to uncover local anomalous attributes, we customize a band-pass spectral GNN message passing module that further enhances the model's generalization capability. Through comprehensive experiments on ten real-world datasets from multiple domains, we validate the effectiveness and robustness of GLADformer. This demonstrates that GLADformer outperforms current state-of-the-art models in graph-level anomaly detection, particularly in effectively capturing global anomaly representations and spectral characteristics.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.00734
- https://arxiv.org/pdf/2406.00734
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399399007
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399399007Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2406.00734Digital Object Identifier
- Title
-
GLADformer: A Mixed Perspective for Graph-level Anomaly DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-02Full publication date if available
- Authors
-
Fan Xu, Nan Wang, Hao Wu, Xuezhi Wen, Dalin Zhang, Siyang Lu, Binyong Li, Wei Gong, Hai Wan, Xibin ZhaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2406.00734Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2406.00734Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2406.00734Direct OA link when available
- Concepts
-
Perspective (graphical), Graph, Anomaly detection, Computer science, Anomaly (physics), Mathematics, Theoretical computer science, Artificial intelligence, Physics, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.generalization | 110 |
| abstract_inverted_index.representations | 148 |
| abstract_inverted_index.characteristics. | 44, 89, 151 |
| abstract_inverted_index.state-of-the-art | 136 |
| abstract_inverted_index.multi-perspective | 51 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |