1. Graph Classification
SEG 5010
Week 3

2. A Summary of Graph Features
Fingerprint
Maccs keys
Tree and cyclic patterns
Frequent subgraphs
Graph fragments

3. A Boosting Approach to Graph Classification (NIPS04)
Apply boosting to graph classification
Weak learner: decision stump
Definition of the gain function
Learning the best weak learner  mining the optimal subgraph
An upper bound of the gain function and branch-and-bound search

4. Leap Search (SIGMOD08)
The first study to mine the optimal subgraph given “general” user-specified objective functions
Vertical pruning: branch-and-bound
An objective function may not be anti-monotone, but its upper bound could be anti-monotone
Horizontal pruning: structural proximity
If two sibling branches are similar in structure, they may be similar in objective function scores
There is a lot of redundancy in the graph pattern search tree

5. COM (CIKM09) Pattern co-occurrences: for effectiveness
Joint discriminative power of multiple graph patterns
Individual subgraphs are not discriminative, but their co-occurrences become discriminative
A different pattern exploration order: for efficiency
Complementary discriminative patterns are examined first
Generate patterns with higher scores before those with lower scores
Rule-based classifiers: a greedy generation process