[2G4-GS-2] Machine learning: pattern extraction

[2G4-GS-2-01] Visualizing the Prediction Basis of Graph Convolution Networks using a Query Learning Algorithm for Ordered Tree Patterns

Naoki Oda¹, 〇Tomoyuki Uchida¹, Takayoshi Shoudai², Satoshi Matsumoto³, Yusuke Suzuki¹, Tetsuhiro Miyahara¹ (1. Hiroshima City University, 2. Fukuoka Institute of Technology, 3. Tokai University)

Keywords:Graph Convolution Networks, Query Learning Algorithm, Ordered Tree Pattern

In this paper, a query learning algorithm visualizing the prediction basis of a trained Graph Convolution Network (GCN) M whose training data is a set D of ordered trees is proposed. The proposed algorithm is based on the query learning model, one of the learning models in computational learning theory, and works with a trained GCN M as an oracle. In more detail, using a constant number of ordered trees F in D, the prediction basis of M is visualized as a representation of the ordered tree pattern by repeating queries to M as an oracle O(n²) times, where n is the maximum number of nodes of ordered trees in F. In addition, for a set D of ordered trees that match the synthetic ordered tree pattern (target pattern), we made a trained GCN M with a subset of D as training data. Then, in order to show effectiveness of the proposed algorithm, we report the ordered tree pattern (pattern for visualization) obtained by executing the proposed algorithm using M as an oracle.