Subgraph Neural Networks

Understand

Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks.

• However, despite the proliferation of the methods and their success, prevailing Graph Neural Networks (GNNs) neglect subgraphs, rendering subgraph prediction tasks challenging to tackle in many impactful applications.
• Further, subgraph prediction tasks present several unique challenges: subgraphs can have non-trivial internal topology, but also carry a notion of position and external connectivity information relative to the underlying graph in which they exist.
• Here, we introduce SubGNN, a subgraph neural network to learn disentangled subgraph representations.