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

Personal use of this material is permitted. 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. T o 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. Index T erms --Multi-label node classification, Semi-supervised attributed graph embedding, Reinforcement learning I. I NTRODUCTION Graph-structured data are frequently witnessed in many real-world applications, such as social graphs and academic graphs. In the graph structure, nodes represent entities (e.g., users in social graphs and papers in citation graphs), whereas edges linking two nodes denote the relationship between the entities (e.g., user friendship and paper citation). Usually both nodes and edges possess their own attributes.