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Graph Classification

Graph Classification. SEG 5010 Week 3. A Summary of Graph Features. Fingerprint Maccs keys Tree and cyclic patterns Frequent subgraphs Graph fragments. A Boosting Approach to Graph Classification (NIPS04). Apply boosting to graph classification Weak learner: decision stump

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Graph Classification

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  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

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