Graph Neural Networks as Multi-View Learning

Haoyu Han, Xiaorui Liu, Haitao Mao, MohamadAli Torkamani, Feng Shi, Victor Lee, Jiliang Tang

Keywords: Graph Neural Networks, Alternating Optimization, Semi-Supervised Learning, Multi-View Learning

TL;DR: A new Multi-View Learning perspective on GNN, which achieves better computation and memory efficiency.

Abstract: Graph Neural Networks (GNNs) have demonstrated powerful representation capability in semi-supervised node classification. In this task, there are often three types of information -- graph structure, node features, and node labels. Existing GNNs usually leverage both node features and graph structure by feature transformation and aggregation, following end-to-end training via node labels. In this paper, we change our perspective by considering these three types of information as three views of nodes. This perspective motivates us to design a new GNN framework as multi-view learning which enables alternating optimization training instead of end-to-end training, resulting in significantly improved computation and memory efficiency. Extensive experiments with different settings demonstrate the effectiveness and efficiency of the proposed method.

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