PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters

Jingyu Chen, Runlin Lei, Zhewei Wei
2/12/2026

No official code found. Best implementation: ChenJY-Count/PolyGCL (20 stars, last commit 2024-03-17).

Semantic Scholar

Code Implementations(1)

ChenJY-Count/PolyGCLCommunity70%

PyTorch implementation of "PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters"

204Shell, PythonMar 10, 20242 years ago

Similar Papers

WMarkGPT: Watermarked Image Understanding via Multimodal Large Language Models

100% match1 repo112026

DataGen: Unified Synthetic Dataset Generation via Large Language Models

100% match1 repo682026

MART: Masked Affective RepresenTation Learning via Masked Temporal Distribution Distillation

100% match1 repo222026

NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes

100% match1 repo252026

ContraDiff: Planning Towards High Return States via Contrastive Learning

100% match2026

COSMIC: Compress Satellite Image Efficiently via Diffusion Compensation

100% match1 repo242026

Cite this paper

@article{chen2026polygcl,
  title  = {PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters},
  author = {Jingyu Chen and Runlin Lei and Zhewei Wei},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:56909c1978d4f62e53365d5237484637a1ac00c8},
  journal = {ICLR 2024 2024}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/56909c1978d4f62e53365d5237484637a1ac00c8.svg)](https://codeofpaper.com/paper/56909c1978d4f62e53365d5237484637a1ac00c8)

Discussion