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)

Paper ID: 13396
Venue: VLDB
Year: 2024
Pagerank: -
Overall Rank: 13,152 | 8.51%
DOI: 10.14778/3654621.3654623

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Rank	Citing Paper	Year	Venue	Pagerank
10,545	OpenFGL: A Comprehensive Benchmark for Federated Graph Learning	2025	VLDB	4.1945683e-05

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