Graph Pattern Mining
Graph Pattern Mining is a computational method that identifies recurring and significant subgraph structures within graph datasets to uncover hidden relational patterns, distinguishing it from broader graph analysis by focusing specifically on the discovery of frequent, meaningful, and potentially predictive graph motifs.
326
Publications
20.6K
Citations
833
Authors
375
Institutions
Publications per year
2017–2026
89
Parents
326
gSpan: graph-based substructure pattern mining
Xifeng Yan, Jiawei Han · 2003 · 2K citations
M. Kuramochi, George Karypis · 2002 · 1.1K citations · Full text
Efficient mining of frequent subgraphs in the presence of isomorphism
Jun Huan, Wei Wang, Jan F. Prins · 2004 · 617 citations
A quickstart in frequent structure mining can make a difference
Siegfried Nijssen, Joost N. Kok · 2004 · 471 citations · Full text
1–5 of 326
833
14
h-index
14
h-index
12
h-index
9
h-index
8
h-index
1–5 of 833
375
University of Illinois Urbana-Champaign
Urbana, United States
15 h-index
31 publications
7.2K citations

University of Edinburgh
Edinburgh, United Kingdom
14 h-index
34 publications
3.6K citations

Harbin Institute of Technology
Harbin, China
12 h-index
28 publications
1.7K citations

Beihang University
Beijing, China
9 h-index
16 publications
651 citations

Centre National de la Recherche Scientifique
Paris, France
9 h-index
25 publications
752 citations
University of California, Santa Barbara
Santa Barbara, United States
9 h-index
14 publications
1K citations
IBM (United States)
Armonk, United States
8 h-index
10 publications
579 citations

National University of Defense Technology
Changsha, China
8 h-index
26 publications
1.2K citations
1–8 of 375
PVLDB · 15 publications · 1.5K citations
15
h-index
TKDE · 15 publications · 793 citations
13
h-index
12 publications · 324 citations
11
h-index
10 publications · 629 citations
10
h-index
6 publications · 251 citations
6
h-index
1–5