GRAPH CLASSIFICATION
BASED ON
PATTERN CO-OCCURRENCE
Ning Jin, Calvin Young, Wei Wang
CIKM 2009
OUTLINES
Motivation
 Objectives
 Methodology
 Experiments
 Conclusions

P.3 – P.4
P.5 – P.6
P.7 – P.17
P.18 – P.19
P.20
2
MOTIVATION


Subgraph patterns are widely used in graph
classification, but their effectiveness is often
hampered by large number of patterns or
lack of discrimination power among
individual patterns.
Enormous pattern space, the graph set may not
have many individual patterns that are
highly discriminative.
3
GRAPH CLASSIFICATION:
EXAMPLE
Negative set:
Positive set:
4
OBJECTIVES


Utilize these graph patterns to derive a
classification model to distinguish between
graphs of different class labels.
Patterns are grouped into co-occurrence rules
during the pattern exploration, leading to
an integrated process of pattern mining and
classifier learning.
5
CO-OCCURRENCE RULES
EXAMPLE
:
Negative set:
Positive set:
6
METHODOLOGY
CAM(Canonical Adjacency Matrix)
 CAM code : Adjacency matrix M
leads to code A1D01E


Each tree node represents a pattern and is a
supergraph of its parent node, with the root node
being an empty graph.
7

CAM tree
8

DEFINITION 1 (Frequency). Given a graph set S,
for a subgraph g, let S’ = {g’ | g’ is in S and g’
supports g}, then the frequency of g is |S’| / |S|.
Negative set:
Positive set:

9

DEFINITION 2 (Discriminative score).
The more discriminative the pattern, the
larger the discrimination score.
define the discrimination score as
:
10
RANK SCORE


Negative set:
Positive set:
11
12
COM EXPLORATION
(CO-OCCURRENCE RULE MINER)
13
Positive set:
14
Positive set:
15
16
17
EXPERIMENTAL RESULTS:
PROTEIN DATASETS
18
EXPERIMENTAL RESULTS:
CHEMICAL DATASETS
10000
Runtime (sec)
1000
100
COM
LEAP
10
1
19
BioAssay ID
CONCLUSIONS


Using heuristic pattern exploration order and
co-occurrences can improve runtime efficiency
of mining discriminative patterns
Using association rules can achieve competitive
classification accuracy
20