1. Graph Database Mining and Its Applications
Luke Huan
Feb 19, 2004

2. Presentation Overview
Introduction
Finding recurring subgraphs from graph databases.
Algorithms
Application
Bioinformatics
Social network
Communication network
Left: social network right protein structure
1L06
11/30/2018

3. An Example of Graph Database Mining
Input: a database of labeled undirected graphs and a threshold 0 <   1 p2 p5 d c a x y
(P) p1 p3 p4 b d c x y
(Q) q1 q3 q2 d c x
(S) s1 s3 s2
 = 2/3
Output: All (connected) frequent subgraphs from the graph database. d c x d c d c x y d c x y
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4. FSG: an apriori like method
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5. Framework
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6. Candidate Generating
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7. How about using paths? Graphs can be decomposed into a set of paths
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8. Applications I: protein structure analysis
ASP 102
ALA 55
SER 214
HIS 57
GLY 140
GLY 142
ASP 194
GLY 43
GLY 196
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9. Application II: Finding social contacts
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10. References
X. Yan, J. Han, gSpan: Graph-Based Substructure Pattern Mining, ICDM 2002
J. Huan, W. Wang, J. Prins. Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism, ICDM 2003.
M. Kuramochi and G. Karypis Finding Frequent Patterns in a Large Sparse Graph, SIAM 2004
N. Vanetik, E. Gudes Mining Frequent Labeled and Partially Labeled Graph Patterns, ICDE 2004
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