Adaptive Propagation Graph Convolutional Network

• URL: http://arxiv.org/abs/2002.10306v3
• Date: Mon, 28 Sep 2020 09:28:34 GMT
• Title: Adaptive Propagation Graph Convolutional Network
• Authors: Indro Spinelli, Simone Scardapane, Aurelio Uncini
• Abstract summary: Graph convolutional networks (GCNs) are a family of neural network models that perform inference on graph data. We show that state-of-the-art results can be achieved by adapting the number of communication steps independently at every node. We show that the proposed adaptive propagation GCN (AP-GCN) achieves superior or similar results to the best proposed models.
• Score: 17.41698818541144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph convolutional networks (GCNs) are a family of neural network models that perform inference on graph data by interleaving vertex-wise operations and message-passing exchanges across nodes. Concerning the latter, two key questions arise: (i) how to design a differentiable exchange protocol (e.g., a 1-hop Laplacian smoothing in the original GCN), and (ii) how to characterize the trade-off in complexity with respect to the local updates. In this paper, we show that state-of-the-art results can be achieved by adapting the number of communication steps independently at every node. In particular, we endow each node with a halting unit (inspired by Graves' adaptive computation time) that after every exchange decides whether to continue communicating or not. We show that the proposed adaptive propagation GCN (AP-GCN) achieves superior or similar results to the best proposed models so far on a number of benchmarks, while requiring a small overhead in terms of additional parameters. We also investigate a regularization term to enforce an explicit trade-off between communication and accuracy. The code for the AP-GCN experiments is released as an open-source library.

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