GBoost: A mathematical programming approach to graph classification and regression

Hiroto Saigo, Sebastian Nowozin, Tadashi Kadowaki, Taku Kudo, Koji Tsuda

Research output: Contribution to journalArticlepeer-review

103 Citations (Scopus)


Graph mining methods enumerate frequently appearing subgraph patterns, which can be used as features for subsequent classification or regression. However, frequent patterns are not necessarily informative for the given learning problem. We propose a mathematical programming boosting method (gBoost) that progressively collects informative patterns. Compared to AdaBoost, gBoost can build the prediction rule with fewer iterations. To apply the boosting method to graph data, a branch-and-bound pattern search algorithm is developed based on the DFS code tree. The constructed search space is reused in later iterations to minimize the computation time. Our method can learn more efficiently than the simpler method based on frequent substructure mining, because the output labels are used as an extra information source for pruning the search space. Furthermore, by engineering the mathematical program, a wide range of machine learning problems can be solved without modifying the pattern search algorithm.

Original languageEnglish
Pages (from-to)69-89
Number of pages21
JournalMachine Learning
Issue number1
Publication statusPublished - Apr 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


Dive into the research topics of 'GBoost: A mathematical programming approach to graph classification and regression'. Together they form a unique fingerprint.

Cite this