Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
This addresses the problem of improving classification accuracy and efficiency in graph-based learning for domains like social networks or recommendation systems, representing an incremental advancement through a novel hybrid method.
The paper tackles semi-supervised multi-label node classification in attributed graphs by proposing a collaborative graph walk method that uses reinforcement learning to tune node representations with available labels, achieving significantly better performance and more efficient graph exploration than state-of-the-art approaches.
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.