GLDL: Graph Label Distribution Learning

Document Type

Conference Paper

Publication Date

2024

DOI

10.1609/aaai.v38i11.29194

Publication Title

Proceedings of the AAAI Conference on Artificial Intelligence

Volume

38

Issue

11

Pages

12965-12974

Conference Name

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

Abstract

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.

Rights

Copyright © 2024, Association for the Advancement of Artificial Intelligence, All rights reserved.

"In the returned rights section of the AAAI copyright form, authors are specifically granted back the right to use their own papers for noncommercial uses, such as inclusion in their dissertations or the right to deposit their own papers in their institutional repositories, provided there is proper attribution. The published version is not available for posting outside the AAAI Digital Library."

Metadata record included in accordance with publisher policy.

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. https://doi.org/10.1609/aaai.v38i11.29194

ORCID

0000-0002-5357-6623 (He)

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