Structural Compression: An Efficient Training Framework for Graph Contrastive Learning

Core Concepts

Structural Compression (StructComp) is a simple yet effective training framework that significantly improves the scalability of graph contrastive learning by substituting message passing with node compression.

Abstract

The paper proposes a novel training framework called Structural Compression (StructComp) to address the scalability challenge of graph contrastive learning (GCL). StructComp is motivated by a sparse low-rank approximation of the diffusion matrix, which allows the encoder to be trained without performing any message passing.
The key idea is to use a graph partition matrix to compress the nodes, such that nodes in the same cluster share the same embedding. This reduces the number of sample pairs needed for the contrastive loss computation and eliminates the need for the encoder to perform message passing during training.
For single-view GCL models, StructComp replaces the GNN encoder with a simpler MLP encoder during training, and then transfers the learned parameters to the original GNN encoder for inference. For multi-view GCL models, StructComp introduces a novel data augmentation method called "DropMember" to generate different representations of the compressed nodes.
The paper provides theoretical analysis to show that the compressed contrastive loss can approximate the original GCL loss, and that StructComp introduces an additional regularization term that makes the encoder more robust.
Extensive experiments on various datasets demonstrate that StructComp significantly reduces the time and memory consumption of GCL training while improving model performance compared to the vanilla GCL models and other scalable training methods.

Stats

The number of nodes in the graph is denoted as n.
The number of clusters in the graph partition is denoted as n'.

Quotes

"StructComp trains the encoder with the compressed nodes. This allows the encoder not to perform any message passing during the training stage, and significantly reduces the number of sample pairs in the contrastive loss."
"We theoretically prove that the original GCL loss can be approximated with the contrastive loss computed by StructComp. Moreover, StructComp can be regarded as an additional regularization term for GCL models, resulting in a more robust encoder."

Key Insights Distilled From

StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning

by Shengzhong Z... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2312.04865.pdf

Deeper Inquiries

How can StructComp be extended to handle dynamic graphs or graphs with evolving structures

StructComp can be extended to handle dynamic graphs or graphs with evolving structures by incorporating adaptive mechanisms for updating the compressed features and graph partition matrix. In the case of dynamic graphs, the compression process can be re-evaluated periodically to account for changes in the graph structure. This re-evaluation can involve re-running the graph partitioning algorithm to adapt to the evolving graph topology. Additionally, the compressed features can be updated based on the latest information available in the dynamic graph. By integrating mechanisms for dynamic feature compression and graph partitioning, StructComp can effectively handle graphs with changing structures.

What are the potential limitations of the low-rank approximation approach used in StructComp, and how can they be addressed

One potential limitation of the low-rank approximation approach used in StructComp is the loss of fine-grained information during the compression process. As the compression aims to merge nodes into clusters, there is a risk of oversimplifying the graph structure and losing important node-level details. To address this limitation, techniques such as hierarchical clustering or adaptive compression thresholds can be employed. Hierarchical clustering can capture multi-level structures in the graph, allowing for a more nuanced compression process. Adaptive compression thresholds can dynamically adjust the level of compression based on the local connectivity and importance of nodes, preserving essential information while reducing computational complexity.

Can the principles of StructComp be applied to other graph-based learning tasks beyond contrastive learning, such as graph classification or link prediction

The principles of StructComp can be applied to other graph-based learning tasks beyond contrastive learning, such as graph classification or link prediction, by adapting the compression and regularization techniques to suit the specific requirements of these tasks. For graph classification, StructComp can be modified to generate compressed representations that capture the discriminative features of different graph classes. This can involve refining the compression process to emphasize class-specific information and incorporating task-specific loss functions during training. Similarly, for link prediction, StructComp can be tailored to preserve the structural properties relevant to predicting links between nodes. By customizing the compression and regularization strategies to the objectives of graph classification and link prediction, StructComp can enhance the efficiency and effectiveness of these tasks.