Dynamic Graph Mining
Dynamic Graph Mining is a subfield of graph mining that focuses on extracting patterns, structures, and knowledge from graphs that evolve over time by explicitly modeling temporal dynamics and structural changes.
103
Publications
5.1K
Citations
342
Authors
170
Institutions
Publications per year
2017–2026
45
Parents
103
Jimeng Sun, Christos Faloutsos, Spiros Papadimitriou et al. · 2007 · 545 citations
Graph-based malware detection using dynamic analysis
Blake Anderson, Daniel Quist, Joshua Neil et al. · Journal in Computer Virology · 2011 · 296 citations
Pattern Mining in Frequent Dynamic Subgraphs
Karsten Borgwardt, Hans‐Peter Kriegel, Peter Wackersreuther · Proceedings · 2006 · 141 citations
1–5 of 103
342
5
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Hong Kong University of Science and Technology, Hong Kong SAR China
3 publications · 89 citations
3
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3
h-index
3
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3
h-index
1–5 of 342
170

Washington State University
Pullman, United States
5 h-index
12 publications
310 citations
Georgia Institute of Technology
Atlanta, United States
4 h-index
13 publications
343 citations
Université Claude Bernard Lyon 1
Villeurbanne, France
4 h-index
7 publications
148 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
8 publications
1.4K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
4 h-index
6 publications
220 citations

Centre National de la Recherche Scientifique
Paris, France
4 h-index
10 publications
289 citations
Ludwig-Maximilians-Universität München
Munich, Germany
3 h-index
8 publications
676 citations
University of Illinois Chicago
Chicago, United States
3 h-index
5 publications
357 citations
1–8 of 170
PVLDB · 5 publications · 272 citations
5
h-index
5 publications · 618 citations
5
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4 publications · 198 citations
4
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TKDD · 4 publications · 77 citations
4
h-index
4 publications · 176 citations
4
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1–5