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Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Dasol Hwang · Jinyoung Park · Sunyoung Kwon · KyungMin Kim · Jung-Woo Ha · Hyunwoo Kim

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1843

Graph neural networks have shown superior performance in a wide range of applications providing a powerful representation of graph-structured data. Recent works show that the representation can be further improved by auxiliary tasks. However, the auxiliary tasks for heterogeneous graphs, which contain rich semantic information with various types of nodes and edges, have less explored in the literature. In this paper, to learn graph neural networks on heterogeneous graphs we propose a novel self-supervised auxiliary learning method using meta paths, which are composite relations of multiple edge types. Our proposed method is learning to learn a primary task by predicting meta-paths as auxiliary tasks. This can be viewed as a type of meta-learning. The proposed method can identify an effective combination of auxiliary tasks and automatically balance them to improve the primary task. Our methods can be applied to any graph neural networks in a plug-in manner without manual labeling or additional data. The experiments demonstrate that the proposed method consistently improves the performance of link prediction and node classification on heterogeneous graphs.

Author Information

Dasol Hwang (Korea University)
Jinyoung Park (Korea University)
Sunyoung Kwon (Pusan National University)
KyungMin Kim (Seoul National University)
Jung-Woo Ha (NAVER AI, NAVER Corp.)
Jung-Woo Ha

- Head, AI Innovation, NAVER Cloud - Research Fellow, NAVER AI Lab - Datasets and Benchmarks Co-Chair, NeurIPS 2023 - Socials Co-Chair, ICML 2023 - Socials Co-Chair, NeurIPS 2022 - BS, Seoul National University - PhD, Seoul National University

Hyunwoo Kim (Korea University)

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