Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing

Hongbin Pei, Yu Li, Huiqi Deng, Jingxin Hai, Pinghui Wang +4 more
2/12/2026

No official code found. Best implementation: XJTU-Graph-Intelligence-Lab/Multi-Track-Message-Passing (9 stars, last commit 2025-05-08).

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Code of MTGCN model for the paper "Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing"

91Shell, PythonJul 2, 20241 years ago

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@article{pei2026multitrack,
  title  = {Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing},
  author = {Hongbin Pei and Yu Li and Huiqi Deng and Jingxin Hai and Pinghui Wang and Jie Ma and Jing Tao and Yuheng Xiong and Xiaohong Guan},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:311a9cc19ca7e10906fbf1a50f3627899ee30bcd},
  journal = {ICML 2024 2024}
}

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