Collaborative graph walk for semi-supervised multi-label node classification

Uchenna Thankgod Akujuobi, Han Yufei, Qiannan Zhang, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.
Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Data Mining (ICDM)
PublisherIEEE
Pages1-10
Number of pages10
ISBN (Print)9781728146041
DOIs
StatePublished - Jan 31 2020

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