GLDL: Graph Label Distribution Learning

Document Type

Conference Paper

Publication Date




Publication Title

Proceedings of the AAAI Conference on Artificial Intelligence







Conference Name

Thirty-Eighth AAAI Conference on Artificial Intelligence, February 20-27, 2024, Vancouver, Canada


Label Distribution Learning (LDL), as a more general learning setting than generic single-label and multi-label learning, has been commonly used in computer vision and many other applications. To date, existing LDL approaches are designed and applied to data without considering the interdependence between instances. In this paper, we propose a Graph Label Distribution Learning (GLDL) framework, which explicitly models three types of relationships: instance-instance, label-label, and instance-label, to learn the label distribution for networked data. A label-label network is learned to capture label-to-label correlation, through which GLDL can accurately learn label distributions for nodes. Dual graph convolution network (GCN) Co-training with heterogeneous message passing ensures two GCNs, one focusing on instance-instance relationship and the other one targeting label-label correlation, are jointly trained such that instance-instance relationship can help induce label-label correlation and vice versa. Our theoretical study derives the error bound of GLDL. For verification, four benchmark datasets with label distributions for nodes are created using common graph benchmarks. The experiments show that considering dependency helps learn better label distributions for networked data, compared to state-of-the-art LDL baseline. In addition, GLDL not only outperforms simple GCN and graph attention networks (GAT) using distribution loss but is also superior to its variant considering label-label relationship as a static network. GLDL and its benchmarks are the first research endeavors to address LDL for graphs. Code and benchmark data are released for public access.


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Original Publication Citation

Jin, Y., Gao, R., He, Y., & Zhu, X. (2024) GLDL: Graph Label Distribution Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(11), 12965-12974. Association for the Advancement of Artificial Intelligence.


0000-0002-5357-6623 (He)