Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification

Abstract

In this work, we study semi-supervised multi-label node classification problem in attributed graphs. Classic solutions to multi-label node classification follow two steps, first learn node embedding and then build a node classifier on the learned embedding. To improve the discriminating power of the node embedding, we propose a novel collaborative graph walk, named Multi-Label-Graph-Walk, to finely tune node representations with the available label assignments in attributed graphs via reinforcement learning. The proposed method formulates the multi-label node classification task as simultaneous graph walks conducted by multiple label-specific agents. Furthermore, policies of the label-wise graph walks are learned in a cooperative way to capture first the predictive relation between node labels and structural attributes of graphs; and second, the correlation among the multiple label-specific classification tasks. A comprehensive experimental study demonstrates that the proposed method can achieve significantly better multi-label classification performance than the state-of-the-art approaches and conduct more efficient graph exploration.

Accepted for IEEE International Conference on Data Mining (ICDM) 2019

References in corpus (7)

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• M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
• Graph Convolutional Reinforcement Learning
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• LNEMLC: Label Network Embeddings for Multi-Label Classification