Graph Anomaly Detection
Graph Anomaly Detection is a computational task within graph analytics that identifies rare, irregular, or suspicious patterns in graph-structured data that deviate significantly from expected structural or behavioral norms.
381
Publications
23.7K
Citations
1.2K
Authors
431
Institutions
Publications per year
2017–2026
232
381
R-MAT: A Recursive Model for Graph Mining
Deepayan Chakrabarti, Yiping Zhan, Christos Faloutsos · 2004 · 1.3K citations · Full text
Caleb C. Noble, Diane J. Cook · 2003 · 471 citations
Shashank Pandit, Duen Horng Chau, Samuel J. Wang et al. · 2007 · 383 citations
1–5 of 381
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431
Carnegie Mellon University
Pittsburgh, United States
35 h-index
98 publications
11.1K citations

Chinese Academy of Sciences
Beijing, China
12 h-index
25 publications
1.2K citations
Arizona State University
Tempe, United States
10 h-index
16 publications
1.7K citations
Tsinghua University
Beijing, China
10 h-index
24 publications
1.5K citations

Zhejiang University
Hangzhou, China
10 h-index
21 publications
932 citations

Washington State University
Pullman, United States
8 h-index
8 publications
549 citations
Tennessee Technological University
Cookeville, United States
8 h-index
10 publications
624 citations
University of Chinese Academy of Sciences
Beijing, China
8 h-index
21 publications
1.1K citations
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TKDE · 15 publications · 740 citations
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Repository · 19 publications · 605 citations
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Conference · 11 publications · 475 citations
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9 publications · 540 citations
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7 publications · 561 citations
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