Graph Classification

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