Timezone: »

 
Poster
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
in Poster Session 2 »

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.

Keywords: graph neural networks · Graph Representation Learning · implicit models · equilibrium models · implicit graph neural networks

Author Information

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

More from the Same Authors