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GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction

Machine Learning 2024-04-03 v3 Social and Information Networks

Abstract

In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model. This model generalizes graph diffusion in different feature spaces, and uses the inception module to avoid the large amount of computations caused by complex network structures. We evaluate GIDN model on Open Graph Benchmark(OGB) datasets, reached an 11% higher performance than AGDN on ogbl-collab dataset.

Keywords

Cite

@article{arxiv.2210.01301,
  title  = {GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction},
  author = {Zixiao Wang and Yuluo Guo and Jin Zhao and Yu Zhang and Hui Yu and Xiaofei Liao and Biao Wang and Ting Yu},
  journal= {arXiv preprint arXiv:2210.01301},
  year   = {2024}
}
R2 v1 2026-06-28T02:44:10.786Z