Improving Graph Neural Network Expressivity via Subgraph Isomorphism
Counting
- URL: http://arxiv.org/abs/2006.09252v3
- Date: Mon, 5 Jul 2021 13:22:05 GMT
- Title: Improving Graph Neural Network Expressivity via Subgraph Isomorphism
Counting
- Authors: Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, Michael M.
Bronstein
- Abstract summary: "Graph Substructure Networks" (GSN) is a topologically-aware message passing scheme based on substructure encoding.
We show that it is strictly more expressive than the Weisfeiler-Leman (WL) graph isomorphism test.
We perform an extensive evaluation on graph classification and regression tasks and obtain state-of-the-art results in diverse real-world settings.
- Score: 63.04999833264299
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While Graph Neural Networks (GNNs) have achieved remarkable results in a
variety of applications, recent studies exposed important shortcomings in their
ability to capture the structure of the underlying graph. It has been shown
that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman
(WL) graph isomorphism test, from which they inherit proven limitations such as
the inability to detect and count graph substructures. On the other hand, there
is significant empirical evidence, e.g. in network science and bioinformatics,
that substructures are often intimately related to downstream tasks. To this
end, we propose "Graph Substructure Networks" (GSN), a topologically-aware
message passing scheme based on substructure encoding. We theoretically analyse
the expressive power of our architecture, showing that it is strictly more
expressive than the WL test, and provide sufficient conditions for
universality. Importantly, we do not attempt to adhere to the WL hierarchy;
this allows us to retain multiple attractive properties of standard GNNs such
as locality and linear network complexity, while being able to disambiguate
even hard instances of graph isomorphism. We perform an extensive experimental
evaluation on graph classification and regression tasks and obtain
state-of-the-art results in diverse real-world settings including molecular
graphs and social networks. The code is publicly available at
https://github.com/gbouritsas/graph-substructure-networks.
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