CARL-G: Clustering-Accelerated Representation Learning on Graphs

Abstract

Self-supervised learning on graphs has made large strides in achieving great performance in various downstream tasks. However, many state-of-the-art methods suffer from a number of impediments, which prevent them from realizing their full potential. For instance, contrastive methods typically require negative sampling, which is often computationally costly. While non-contrastive methods avoid this expensive step, most existing methods either rely on overly complex architectures or dataset-specific augmentations. In this paper, we ask: Can we borrow from classical unsupervised machine learning literature in order to overcome those obstacles? Guided by our key insight that the goal of distance-based clustering closely resembles that of contrastive learning: both attempt to pull representations of similar items together and dissimilar items apart. As a result, we propose CARL-G - a novel clustering-based framework for graph representation learning that uses a loss inspired by Cluster Validation Indices (CVIs), i.e., internal measures of cluster quality (no ground truth required). CARL-G is adaptable to different clustering methods and CVIs, and we show that with the right choice of clustering method and CVI, CARL-G outperforms node classification baselines on 4/5 datasets with up to a 79x training speedup compared to the best-performing baseline. CARL-G also performs at par or better than baselines in node clustering and similarity search tasks, training up to 1,500x faster than the best-performing baseline. Finally, we also provide theoretical foundations for the use of CVI-inspired losses in graph representation learning.

14 pages. Accepted at KDD 2023

References in corpus (10)

• Comparing community structure identification
• Predicting multicellular function through multi-layer tissue networks
• Deep Graph Contrastive Representation Learning
• Contrastive Multi-View Representation Learning on Graphs
• ETA Prediction with Graph Neural Networks in Google Maps
• Few-Shot Graph Learning for Molecular Property Prediction
• From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
• Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
• Understanding Negative Sampling in Graph Representation Learning
• Clustering by Direct Optimization of the Medoid Silhouette