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| Attribute Missing Graph Learning Method Based on the Structural Joint Bipartite Network |
| HAN Zhongming, ZHANG Shuqun, LIU Yan, HU Qiwen, YANG Weijie
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| School of Computer and ArtificialIntelligence, Beijing Technology and Business University, Beijing 100048, China |
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Abstract Aiming at the problem of missing node attributes in graph data, we proposes a novel attribute missing graph learning framework. The framework maps node attributes to edge attributes by reconstructing the structural joint bipartite network. This enables attribute completion and graph tasks to be performed together under a unified framework that can handle both continuous and discrete missing data. According to the attribute homogeneity and structural homogeneity of the attribute graph, we propose an attribute missing representation learning method, which introduces edge embeddings and attention mechanisms to capture the correlations between attribute-attribute and structure-attribute in structural joint bipartite network to enhance the missing attribute learning. Experimental results on four real-world datasets show that our framework outperforms the baselines in both attribute completion tasks, validating the effectiveness of the method.
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Received: 08 April 2025
Published: 03 June 2025
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