Towards generalizable Graph Contrastive Learning: An information theory perspective.

Area of Science:

• Machine Learning
• Graph Representation Learning
• Artificial Intelligence

Background:

• Graph Contrastive Learning (GCL) learns representations from pretext tasks for downstream applications.
• Current GCL methods struggle with generalization to diverse, unpredictable downstream tasks.
• The theoretical underpinnings of GCL generalization remain underexplored.

Purpose of the Study:

• To introduce a novel metric, GCL-GE, for quantifying the generalization gap in GCL.
• To develop a GCL framework that enhances generalization ability.
• To provide theoretical insights into improving GCL's adaptability.

Main Methods:

• Leveraging information theory to derive a downstream-task-independent mutual information upper bound for GCL-GE.
• Proposing InfoAdv, a GCL framework that jointly optimizes GCL-GE and InfoMax.
• Conducting extensive experiments to validate the framework's performance.

Main Results:

• InfoAdv effectively enhances performance across a wide range of downstream tasks.
• The proposed GCL-GE metric successfully quantifies the generalization gap.
• The framework demonstrates improved generalizability of GCL.

Conclusions:

• InfoAdv offers a principled approach to improving GCL generalization.
• The developed metric and framework address key limitations in current GCL methods.
• This work advances the field of graph representation learning by enhancing model adaptability.