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Oral
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija · Bryan Perozzi · Amol Kapoor · Nazanin Alipourfard · Kristina Lerman · Hrayr Harutyunyan · Greg Ver Steeg · Aram Galstyan

Wed Jun 12 05:05 PM -- 05:10 PM (PDT) @ Grand Ballroom
in Representation Learning »

In this work, we show that popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) do not model and cannot learn a class of general neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can capture these difference relationships by learning mixed feature representations of neighbors at various distances. MixHop requires no additional memory or computational complexity, and outperforms challenging baselines on several graph datasets. In addition, we propose a sparsity regularization that allows us to visualize how the network prioritizes neighborhood information across different graph datasets. Our analysis of the learned parameters reveals that different datasets utilize neighborhood mixing in different ways

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Author Information

Sami Abu-El-Haija (USC Information Sciences Institute)
Bryan Perozzi (Google AI)
Amol Kapoor (Google Research)
Nazanin Alipourfard (University of Southern California)
Kristina Lerman (ISI, University of Southern California)
Hrayr Harutyunyan (University of Southern California)
Greg Ver Steeg (University of Southern California)
Aram Galstyan (USC ISI)

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