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Neighborhood Based Fast Graph Search in Large Networks

Summary: Proposes Ness, a neighborhood-based similarity for top-k approximate subgraph/graph matching in large labeled networks, avoiding isomorphism and edit-distance. An information-propagation model embeds networks into vectors for fast indexing; robust to noise, with subgraph match NP-hard, graph similarity polynomial. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4503
Venue
SIGMOD
Year
2011
Pagerank
0.0001093153
Overall Rank
1,394 | 90.44%
DOI
10.1145/1989323.1989418

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{khan_sigmod11,
        title = {{Neighborhood Based Fast Graph Search in Large Networks}},
        author = {Khan, Arijit and Li, Nan and Yan, Xifeng and Guan, Ziyu and Chakraborty, Supriyo and Tao, Shu},
        series = {{SIGMOD} '11},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1989323.1989418},
        url = {https://dl.acm.org/doi/10.1145/1989323.1989418},
        year = {2011}
}

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