Graph Outlier Detection
Graph Outlier Detection is an outlier detection paradigm that specifically identifies anomalous instances within graph-structured data by exploiting relational topology and attribute dependencies distinct from conventional feature-based or distance-based outlier detection methods.
160
Publications
10.5K
Citations
477
Authors
211
Institutions
Publications per year
2017–2026
80
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Carnegie Mellon University
Pittsburgh, United States
11 h-index
18 publications
2.7K citations
University of Illinois Urbana-Champaign
Urbana, United States
7 h-index
17 publications
1.8K citations
Stony Brook University
Stony Brook, United States
6 h-index
8 publications
2.6K citations
Massachusetts Institute of Technology
Cambridge, United States
6 h-index
13 publications
544 citations
Tsinghua University
Beijing, China
5 h-index
14 publications
1.1K citations

Zhejiang University
Hangzhou, China
5 h-index
11 publications
774 citations
Karlsruhe Institute of Technology
Karlsruhe, Germany
4 h-index
15 publications
1.6K citations

Macquarie University
Sydney, Australia
4 h-index
18 publications
1.1K citations
1–8 of 211
Repository · 10 publications · 316 citations
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Conference · 8 publications · 425 citations
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4 publications · 163 citations
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3 publications · 67 citations
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PVLDB · 2 publications · 131 citations
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