Collaborative graph walk for semi-supervised multi-label node classification
Type
Conference PaperKAUST Department
Computer Science ProgramKing Abdullah University of Science and Technology (KAUST), Saudi Arabia
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2020-01-31Permanent link to this record
http://hdl.handle.net/10754/660643
Metadata
Show full item recordAbstract
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.Citation
Akujuobi, U., Yufei, H., Zhang, Q., & Zhang, X. (2019). Collaborative Graph Walk for Semi-Supervised Multi-label Node Classification. 2019 IEEE International Conference on Data Mining (ICDM). doi:10.1109/icdm.2019.00010Sponsors
This work is supported by the King Abdullah University of Science and Technology (KAUST), Saudi ArabiaPublisher
IEEEConference/Event name
19th IEEE International Conference on Data Mining, ICDM 2019arXiv
1910.09706Additional Links
https://ieeexplore.ieee.org/document/8970680/ae974a485f413a2113503eed53cd6c53
10.1109/ICDM.2019.00010