Efficient Betweenness Centrality Computation over Large Heterogeneous Information Networks
Summary: Introduces the first meta-path-based formulation of type-specific betweenness centrality on heterogeneous information networks, defining coarse-grained and fine-grained BC (cBC, fBC). Provides a generalized algorithm and optimizations (network compression, BFS-DAG sharing) to scale cBC/fBC, validated on real HINs. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Xinrui Wang (Shandong University)
- 2. Yiran Wang (Shandong University)
- 3. Xuemin Lin (Shanghai Jiao Tong University)
- 4. Jeffrey Xu Yu (Chinese University of Hong Kong)
- 5. Hong Gao (Zhejiang Normal University)
- 6. Xiuzhen Cheng (Shandong University)
- 7. Dongxiao Yu (Shandong University)
BibTeX Citation
@article{wang_vldb24,
title = {{Efficient Betweenness Centrality Computation over Large Heterogeneous Information Networks}},
author = {Wang, Xinrui and Wang, Yiran and Lin, Xuemin and Yu, Jeffrey Xu and Gao, Hong and Cheng, Xiuzhen and Yu, Dongxiao},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3360--3372},
doi = {10.14778/3681954.3682006},
url = {https://doi.org/10.14778/3681954.3682006},
year = {2024}
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| Rank | Citing Paper | Year | Venue | Pagerank |
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| 9,622 | Revisiting Graph Analytics Benchmark | 2025 | SIGMOD | 5.2434488e-05 |
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