Efficient Approximation of Kemeny’s Constant for Large Graphs
Summary: Scalable approximation of Kemeny’s constant for massive graphs via two Monte Carlo methods. RefinedMC trims redundant truncated random walks; ForestMC leverages a Laplacian-submatrix / forest-based identity for higher-accuracy estimation, avoiding explicit matrix inversion. (summarized by gpt-5.4-mini on May 24 2026)
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Authors
- 1. Haisong Xia (Fudan University)
- 2. Zhongzhi Zhang (Fudan University)
BibTeX Citation
@inproceedings{xia_sigmod24,
title = {{Efficient Approximation of Kemeny’s Constant for Large Graphs}},
author = {Xia, Haisong and Zhang, Zhongzhi},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654937},
url = {https://dl.acm.org/doi/10.1145/3654937},
year = {2024}
}
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