LGM: Mining Frequent Subgraphs from Linear Graphs - Google Sites
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LGM: Mining Frequent Subgraphs from Linear Graphs - Google Sites
Koji Tsuda (AIST). The 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2011). 25 May 2011. LGM
The 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2011) 25 May 2011
LGM: Mining Frequent Subgraphs from Linear Graphs Yasuo Tabei
ERATO Minato Project Japan Science and Technology Agency joint work with Daisuke Okanohara (Preferred Infrastructure), Shuichi Hirose (AIST), Koji Tsuda (AIST) 1 1
Outline • Introduction to linear graph ★
Linear subgraph relation
★
Total order among edges
• Frequent subgraph mining from a set of linear graphs
• Experiments ★
Motif extraction from protein 3D structures 2
2
Linear graph (Davydov et al., 2004)
• Labeled graph whose vertices are totally ordered g = (V, E, L , L ) Linear graph • V
E
‣ V ⊂ N : ordered vertex set ‣ E ⊆ V × V : edge set ‣ LV → ΣV : vertex labels E E : edge labels ‣L →Σ Example: c
b
1 A
a
a
2 B
3
4 B
A
5 C
6 A
3 3
Linear subgraph relation
•
g1 is a linear subgraph of g2
i) Conventional subgraph condition ★ Vertex labels are matched ★ All edges of g1 exist in g2 with the correct labels
ii) Order of vertices are conserved Example:
b
b
1 A
a
2 B
g1
3 A
⊂
c
a
1 A
2 A
3 B
a
4
g2
B
5 C
6 A
4 4
•
•
Subgraph but not linear subgraph g1 is a subgraph of g2 ★ vertex labels are matched ★ all edges in g1also exist in g2 with correct labels g1 is not a linear subgraph of g2 ★
