A Collective Learning Framework to Boost GNN Expressiveness

• URL: http://arxiv.org/abs/2003.12169v2
• Date: Mon, 28 Sep 2020 20:42:07 GMT
• Title: A Collective Learning Framework to Boost GNN Expressiveness
• Authors: Mengyue Hang, Jennifer Neville, Bruno Ribeiro
• Abstract summary: We consider the task of inductive node classification using Graph Neural Networks (GNNs) in supervised and semi-supervised settings. We propose a general collective learning approach to increase the representation power of any existing GNN. We evaluate performance on five real-world network datasets and demonstrate consistent, significant improvement in node classification accuracy.
• Score: 25.394456460032625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have recently been used for node and graph classification tasks with great success, but GNNs model dependencies among the attributes of nearby neighboring nodes rather than dependencies among observed node labels. In this work, we consider the task of inductive node classification using GNNs in supervised and semi-supervised settings, with the goal of incorporating label dependencies. Because current GNNs are not universal (i.e., most-expressive) graph representations, we propose a general collective learning approach to increase the representation power of any existing GNN. Our framework combines ideas from collective classification with self-supervised learning, and uses a Monte Carlo approach to sampling embeddings for inductive learning across graphs. We evaluate performance on five real-world network datasets and demonstrate consistent, significant improvement in node classification accuracy, for a variety of state-of-the-art GNNs.

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