Node Masking: Making Graph Neural Networks Generalize and Scale Better
- URL: http://arxiv.org/abs/2001.07524v4
- Date: Sun, 16 May 2021 19:40:24 GMT
- Title: Node Masking: Making Graph Neural Networks Generalize and Scale Better
- Authors: Pushkar Mishra, Aleksandra Piktus, Gerard Goossen, Fabrizio Silvestri
- Abstract summary: Graph Neural Networks (GNNs) have received a lot of interest in the recent times.
In this paper, we utilize some theoretical tools to better visualize the operations performed by state of the art spatial GNNs.
We introduce a simple concept, Node Masking, that allows them to generalize and scale better.
- Score: 71.51292866945471
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph Neural Networks (GNNs) have received a lot of interest in the recent
times. From the early spectral architectures that could only operate on
undirected graphs per a transductive learning paradigm to the current state of
the art spatial ones that can apply inductively to arbitrary graphs, GNNs have
seen significant contributions from the research community. In this paper, we
utilize some theoretical tools to better visualize the operations performed by
state of the art spatial GNNs. We analyze the inner workings of these
architectures and introduce a simple concept, Node Masking, that allows them to
generalize and scale better. To empirically validate the concept, we perform
several experiments on some widely-used datasets for node classification in
both the transductive and inductive settings, hence laying down strong
benchmarks for future research.
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