LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
Summary: LightDiC: scalable digraph convolution via the magnetic Laplacian, moves topology work to offline preprocessing so downstream training is non‑recursive and efficient at large scale. Proves complex-field message passing ≈ proximal gradient descent on Dirichlet energy (digraph denoising), yielding strong expressiveness; matches or beats SOTA with fewer parameters. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Xunkai Li (Beijing Institute of Technology)
- 2. Meihao Liao (Beijing Institute of Technology)
- 3. Zhengyu Wu (Beijing Institute of Technology)
- 4. Daohan Su (Beijing Institute of Technology)
- 5. Wentao Zhang (Peking University)
- 6. Rong-Hua Li (Beijing Institute of Technology)
- 7. Guoren Wang (Beijing Institute of Technology)
BibTeX Citation
@article{li_vldb24,
title = {{LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning}},
author = {Li, Xunkai and Liao, Meihao and Wu, Zhengyu and Su, Daohan and Zhang, Wentao and Li, Rong-Hua and Wang, Guoren},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {7},
pages = {1542--1551},
doi = {10.14778/3654621.3654623},
url = {https://doi.org/10.14778/3654621.3654623},
year = {2024}
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| Rank | Citing Paper | Year | Venue | Pagerank |
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| 10,817 | OpenFGL: A Comprehensive Benchmark for Federated Graph Learning | 2025 | VLDB | 5.093636e-05 |
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