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GAIA: Graph Classification Using Evolutionary Computation

Summary: GAIA mines discriminative subgraphs for large graphs using a novel encoding and an evolutionary search over pattern space. GAIA produces graph classifiers from mined patterns; outperforms state-of-the-art in accuracy and runtime. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4301
Venue
SIGMOD
Year
2010
Pagerank
4.9349071e-05
Overall Rank
6,760 | 52.98%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
6,801 Updating Graph Indices with a One-Pass Algorithm 2015 SIGMOD 4.9226813e-05
8,210 Mining Top-k Pairs of Correlated Subgraphs in a Large Network 2020 VLDB 4.5581054e-05
9,057 Behavior Query Discovery in System-Generated Temporal Graphs 2016 VLDB 4.4039656e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,747 Mining Significant Graph Patterns by Leap Search 2008 SIGMOD 0.00010691242
4,716 Mining Graph Patterns Efficiently via Randomized Summaries 2009 VLDB 5.9755569e-05
5,436 Output Space Sampling for Graph Patterns 2009 VLDB 5.5042223e-05
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