Subgraph Neural Networks

06/18/2020
by   Emily Alsentzer, et al.
20

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, because 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 SUB-GNN, a subgraph neural network to learn disentangled subgraph representations. In particular, we propose a novel subgraph routing mechanism that propagates neural messages between the subgraph's components and randomly sampled anchor patches from the underlying graph, yielding highly accurate subgraph representations. SUB-GNN specifies three channels, each designed to capture a distinct aspect of subgraph structure, and we provide empirical evidence that the channels encode their intended properties. We design a series of new synthetic and real-world subgraph datasets. Empirical results for subgraph classification on eight datasets show that SUB-GNN achieves considerable performance gains, outperforming strong baseline methods, including node-level and graph-level GNNs, by 12.4 baseline. SUB-GNN performs exceptionally well on challenging biomedical datasets when subgraphs have complex topology and even comprise multiple disconnected components.

READ FULL TEXT

page 8

page 9

page 11

page 12

page 14

page 15

page 17

page 18

research
10/25/2021

Nested Graph Neural Networks

Graph neural network (GNN)'s success in graph classification is closely ...
research
09/22/2020

GraphCrop: Subgraph Cropping for Graph Classification

We present a new method to regularize graph neural networks (GNNs) for b...
research
12/23/2022

Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection

Graph neural networks (GNNs) have received remarkable success in link pr...
research
12/30/2021

Deconfounded Training for Graph Neural Networks

Learning powerful representations is one central theme of graph neural n...
research
01/31/2022

SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning

Graph Neural Networks (GNNs) have demonstrated a great potential in a va...
research
11/01/2022

Position-Aware Subgraph Neural Networks with Data-Efficient Learning

Data-efficient learning on graphs (GEL) is essential in real-world appli...
research
04/01/2021

Sub-GMN: The Subgraph Matching Network Model

As one of the most fundamental tasks in graph theory, subgraph matching ...

Please sign up or login with your details

Forgot password? Click here to reset