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)
Incoming Non-self Citations Over Time
Authors
- 1. Arijit Khan (University of California Santa Barbara)
- 2. Nan Li (University of California Santa Barbara)
- 3. Xifeng Yan (University of California Santa Barbara)
- 4. Ziyu Guan (University of California Santa Barbara)
- 5. Supriyo Chakraborty (University of California Los Angeles)
- 6. Shu Tao (IBM)
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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