Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information Networks
Summary: Efficient maximal motif-clique enumeration in large HINs via an order-based duplicate-free framework, pivot pruning, and optimized candidate/subgraph processing. Experiments on five real networks show up to 10³× speedups over the state of the art. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yingli Zhou (Chinese University of Hong Kong)
- 2. Yixiang Fang (Chinese University of Hong Kong)
- 3. Chenhao Ma (Chinese University of Hong Kong)
- 4. Tianci Hou (Chinese University of Hong Kong)
- 5. Xin Huang (Hong Kong Baptist University)
BibTeX Citation
@article{zhou_vldb24,
title = {{Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information Networks}},
author = {Zhou, Yingli and Fang, Yixiang and Ma, Chenhao and Hou, Tianci and Huang, Xin},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {2946--2959},
doi = {10.14778/3681954.3681975},
url = {https://doi.org/10.14778/3681954.3681975},
year = {2024}
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