Papers › Diffusion-Convolutional Neural Networks

Diffusion-Convolutional Neural Networks

6 Nov 2015NeurIPS 2016 12arXiv:1511.02136archive 2025-07-28

James Atwood, Don Towsley

We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graph-structured data and used as an effective basis for node classification. DCNNs have several attractive qualities, including a latent representation for graphical data that is invariant under isomorphism, as well as polynomial-time prediction and learning that can be represented as tensor operations and efficiently implemented on the GPU. Through several experiments with real structured datasets, we demonstrate that DCNNs are able to outperform probabilistic relational models and kernel-on-graph methods at relational node classification tasks.

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Methods

Introduced by this paper: DCNN

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