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MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Juncheng Liu · Bryan Hooi · Kenji Kawaguchi · Xiaokui Xiao

Tue Nov 29 02:00 PM -- 04:00 PM (PST) @ Hall J #341

Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. In this paper, we introduce and justify two weaknesses of implicit GNNs: the constrained expressiveness due to their limited effective range for capturing long-range dependencies, and their lack of ability to capture multiscale information on graphs at multiple resolutions. To show the limited effective range of previous implicit GNNs, We first provide a theoretical analysis and point out the intrinsic relationship between the effective range and the convergence of iterative equations used in these models. To mitigate the mentioned weaknesses, we propose a multiscale graph neural network with implicit layers (MGNNI) which is able to model multiscale structures on graphs and has an expanded effective range for capturing long-range dependencies. We conduct comprehensive experiments for both node classification and graph classification to show that MGNNI outperforms representative baselines and has a better ability for multiscale modeling and capturing of long-range dependencies.

Author Information

Juncheng Liu (National University of Singapore)
Bryan Hooi (National University of Singapore)
Kenji Kawaguchi (MIT)
Xiaokui Xiao (National University of Singapore)

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