ICML 2025

ML2-GCL: Manifold Learning Inspired Lightweight Graph Contrastive
Learning
Jianqing Liang 1 Zhiqiang Li 1 Xinkai Wei 1 Yuan Liu 1 Zhiqiang Wang 1
Abstract
Graph contrastive learning has attracted great
interest as a dominant and promising self-
supervised representation learning approach in
recent years. While existing works follow the ba-
sic principle of pulling positive pairs closer and
pushing negative pairs far away, they still suffer
from several critical problems, such as the under-
lying semantic disturbance brought by augmen-
tation strategies, the failure of GCN in capturing
long-range dependence, rigidness and inefficiency
of node sampling techniques. To address these
issues, we propose Manifold Learning Inspired
Lightweight Graph Contrastive Learning (ML2-
GCL), which inherits the merits of both manifold
learning and GCN. ML2-GCL avoids the poten-
tial risks of semantic disturbance with only one
single view. It achieves global nonlinear struc-
ture recovery from locally linear fits, which can
make up for the defects of GCN. The most amaz-
ing advantage is about the lightweight due to its
closed-form solution of positive pairs weights and
removal of pairwise distances calculation. The-
oretical analysis proves the existence of the op-
timal closed-form solution. Extensive empirical
results on various benchmarks and evaluation pro-
tocols demonstrate effectiveness and lightweight
of ML2-GCL. We release the code at https:
//github.com/a-hou/ML2-GCL.
1. Introduction
Graph representation learning, which aims to learn appro-
priate low-dimensional representations for graph structured
*Equal contribution 1Key Laboratory of Computational Intel-
ligence and Chinese Information Processing of Ministry of Edu-
cation, School of Computer and Information Technology, Shanxi
University, Taiyuan 030006, Shanxi, China. Correspondence to:
Jianqing Liang <liangjq@sxu.edu.cn>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267,...

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