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arXiv · 1912.06058

Coloring graph neural networks for node disambiguation

George Dasoulas·Ludovic Dos Santos·Kevin Scaman·Aladin Virmaux
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Abstract

In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks(MPNNs). More specifically, we introduce a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate identical node attributes, and show that this representation is a universal approximator of continuous functions on graphs with node attributes. Our method relies on separability , a key topological characteristic that allows to extend well-chosen neural networks into universal representations. Finally, we show experimentally that CLIP is capable of capturing structural characteristics that traditional MPNNs fail to distinguish,while being state-of-the-art on benchmark graph classification datasets.

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George Dasoulas, Ludovic Dos Santos, Kevin Scaman, Aladin Virmaux. 2019-12-12. Coloring graph neural networks for node disambiguation. https://arxiv.org/abs/1912.06058

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