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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 4: AAAI-22 Technical Tracks 4

A Self-Supervised Mixed-Curvature Graph Neural Network

February 1, 2023

Authors

Li Sun

North China Electric Power University


Zhongbao Zhang

Beijing University of Posts and Telecommunications


Junda Ye

Beijing University of Posts and Telecommunications


Hao Peng

Beihang University


Jiawei Zhang

University of California Davis


Sen Su

Beijing University of Posts and Telecommunications


Philip S Yu

University of Illinois at Chicago


Proceedings:

No. 4: AAAI-22 Technical Tracks 4

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Data Mining and Knowledge Management

Downloads:

Download PDF

Abstract:

Graph representation learning received increasing attentions in recent years. Most of the existing methods ignore the complexity of the graph structures and restrict graphs in a single constant-curvature representation space, which is only suitable to particular kinds of graph structure indeed. Additionally, these methods follow the supervised or semi-supervised learning paradigm, and thereby notably limit their deployment on the unlabeled graphs in real applications. To address these aforementioned limitations, we take the first attempt to study the self-supervised graph representation learning in the mixed-curvature spaces. In this paper, we present a novel Self-Supervised Mixed-Curvature Graph Neural Network (SelfMGNN). To capture the complex graph structures, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces, and design hierarchical attention mechanisms for learning and fusing graph representations across these component spaces. To enable the self-supervised learning, we propose a novel dual contrastive approach. The constructed mixed-curvature space actually provides multiple Riemannian views for the contrastive learning. We introduce a Riemannian projector to reveal these views, and utilize a well-designed Riemannian discriminator for the single-view and cross-view contrastive learning within and across the Riemannian views. Finally, extensive experiments show that SelfMGNN captures the complex graph structures and outperforms state-of-the-art baselines.

DOI:

10.1609/aaai.v36i4.20333


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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