Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2019
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| _version_ | 1866917611946639360 |
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| author | Akujuobi, Uchenna Yufei, Han Zhang, Qiannan Zhang, Xiangliang |
| author_facet | Akujuobi, Uchenna Yufei, Han Zhang, Qiannan Zhang, Xiangliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1910_09706 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification Akujuobi, Uchenna Yufei, Han Zhang, Qiannan Zhang, Xiangliang Machine Learning Artificial Intelligence 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. |
| title | Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/1910.09706 |