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Title: Efficient Contrastive Learning for Fast and Accurate Inference on Graphs
Graph contrastive learning has made remarkable advances in settings where there is a scarcity of task-specific labels. Despite these advances, the significant computational overhead for representation inference incurred by existing methods that rely on intensive message passing makes them unsuitable for latency-constrained applications. In this paper, we present GraphECL, a simple and efficient contrastive learning method for fast inference on graphs. GraphECL does away with the need for expensive message passing during inference. Specifically, it introduces a novel coupling of the MLP and GNN models, where the former learns to computationally efficiently mimic the computations performed by the latter. We provide a theoretical analysis showing why MLP can capture essential structural information in neighbors well enough to match the performance of GNN in downstream tasks. The extensive experiments on widely used real-world benchmarks that show that GraphECL achieves superior performance and inference efficiency compared to state-of-the-art graph constrastive learning (GCL) methods on homophilous and heterophilous graphs. Code is available at: https: //github.com/tengxiao1/GraphECL.  more » « less
Award ID(s):
2226025 2041759
PAR ID:
10549103
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
PMLR
Date Published:
Journal Name:
Proceedings of Machine Learning Research: International Conference on Machine Learning
Volume:
235
ISSN:
2640-3498
Page Range / eLocation ID:
54363-54381
Subject(s) / Keyword(s):
graph neural networks, inference, contrastive learning, machine learning
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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