Diagonally Subgraphs Pattern Mining

Moti Cohen Ehud GudesDMKD’04

Introduction

Hybrid gSpan-DFS Code tree-based FSG – BFS Apriori

Reverse Depth Search

Lexicographic Ordering in graphs

01XaY 12YbX 13YbZ 30ZcX 34ZbX

Minimum DFS Code

X

Y

Z

a

b

c

X

X

b

b

V0

V4

V3V2

V1

Algorithm

Prefix Based Lattice Reverse Depth Search

Graph database

X

Frequent1 edge: xax,xaz,xbx,xby,xbz,ycz

xby-prefix tid{1,2,3} F:{xby,xbz,ycz}

b

bx

y

xc

bx

y

z

cy

z

bx

z

bx

y

c

bx

y

z

c

z

b

bx

y

x

b

xcb

bx

y

x z

{<c1,ycz>}

{<c1,xby>}

{<c1,ycz>,<c2,ybx>}

cb

bx

y

x z

tid{1}tid{1,2}

Sup:1Sup:2

tid{1}tid{1,2}

Sup:0

tid{1,2} tid{1,2

}

Sup:1Sup:1

Sup:2Sup:2

Candidates Generation k+1

Join two frequent k-subgraphs Contain the same k-1-subgraph as core

Extension(k-1subgraph): {(ksub,extend edge),(),..,()} Join Ri and R with core Qj

Qj Subpatterns(R) Ri Extension(Qj)

extension

Freq. Anti-Monotone Pruning

all its k-subgraphs are frequent Massive computation

Partial FAM Almost as good as FAM

Drop gk+1candidates generated times < |subpattern(gk)|

Experiment

Intel 2.0GHz 256MB winxp vc++6.0

Synthetic minsup = 1%

Chemical Compound dataset