Measuring and Sampling: A Metric-guided Subgraph Learning Framework for Graph Neural Network

by   Jiyang Bai, et al.

Graph neural network (GNN) has shown convincing performance in learning powerful node representations that preserve both node attributes and graph structural information. However, many GNNs encounter problems in effectiveness and efficiency when they are designed with a deeper network structure or handle large-sized graphs. Several sampling algorithms have been proposed for improving and accelerating the training of GNNs, yet they ignore understanding the source of GNN performance gain. The measurement of information within graph data can help the sampling algorithms to keep high-value information while removing redundant information and even noise. In this paper, we propose a Metric-Guided (MeGuide) subgraph learning framework for GNNs. MeGuide employs two novel metrics: Feature Smoothness and Connection Failure Distance to guide the subgraph sampling and mini-batch based training. Feature Smoothness is designed for analyzing the feature of nodes in order to retain the most valuable information, while Connection Failure Distance can measure the structural information to control the size of subgraphs. We demonstrate the effectiveness and efficiency of MeGuide in training various GNNs on multiple datasets.


page 1

page 18

page 19


Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network

Graph neural networks (GNNs) have achieved outstanding performance in le...

Measuring and Improving the Use of Graph Information in Graph Neural Networks

Graph neural networks (GNNs) have been widely used for representation le...

Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization

Graph Neural Networks (GNNs) have emerged as a powerful category of lear...

Efficiently Counting Substructures by Subgraph GNNs without Running GNN on Subgraphs

Using graph neural networks (GNNs) to approximate specific functions suc...

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a f...

Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

Social events provide valuable insights into group social behaviors and ...

Improved Aggregating and Accelerating Training Methods for Spatial Graph Neural Networks on Fraud Detection

Graph neural networks (GNNs) have been widely applied to numerous fields...

Please sign up or login with your details

Forgot password? Click here to reset