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Shortest Paths Discovery in Uncertain Networks via Transfer Learning

Summary: Uncertain graphs with probabilistic edges: finding the most probable shortest path between node pairs. Presents a fast non-learning Phase 2 sampler and a transfer-learning based predictor that estimates path probabilities without costly simulations, achieving up to 5x–210x speedups and scalable cross-domain generalization. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6706
Venue
SIGMOD
Year
2023
Pagerank
5.3251649e-05
Overall Rank
9,062 | 37.83%
DOI
10.1145/3589286

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{huang_sigmod23,
        title = {{Shortest Paths Discovery in Uncertain Networks via Transfer Learning}},
        author = {Huang, Shixun and Bao, Zhifeng},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3589286},
        url = {https://dl.acm.org/doi/10.1145/3589286},
        year = {2023}
}

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