Complex-Path: Effective and Efficient Node Ranking with Paths in Billion-Scale Heterogeneous Graphs
Summary: CompNode introduces Complex-path, a unified schema for nonlinear conditional paths plus pre-aggregation to drastically reduce HGNN memory and sampling costs. Fusing aggregated complex-path semantics greatly improves node ranking (+20% AP vs SOTA HGNNs; +252% success rate vs a graph-database baseline). (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Jinquan Hang (JD Group; Rutgers University)
- 2. Zhiqing Hong (Rutgers University)
- 3. Xinyue Feng (Rutgers University)
- 4. Guang Wang (Florida State University)
- 5. Dongjiang Cao (JD Group)
- 6. Jiayang Qiao (JD Group)
- 7. Haotian Wang (JD Group)
- 8. Desheng Zhang (Rutgers University)
BibTeX Citation
@article{hang_vldb24,
title = {{Complex-Path: Effective and Efficient Node Ranking with Paths in Billion-Scale Heterogeneous Graphs}},
author = {Hang, Jinquan and Hong, Zhiqing and Feng, Xinyue and Wang, Guang and Cao, Dongjiang and Qiao, Jiayang and Wang, Haotian and Zhang, Desheng},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {3973--3986},
doi = {10.14778/3685800.3685820},
url = {https://doi.org/10.14778/3685800.3685820},
year = {2024}
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