NeMa: Fast Graph Search with Label Similarity
Summary: NeMa: neighborhood-based subgraph search for labeled, heterogeneous networks. It defines a unified node-level cost combining structure and label similarity to yield top-k matches; NP-hard, solved with a heuristic inference-model and optimizations, outperforming keyword search and baselines. (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. Yinghui Wu (University of California Santa Barbara)
- 3. Charu C. Aggarwal (IBM)
- 4. Xifeng Yan (University of California Santa Barbara)
BibTeX Citation
@article{khan_vldb13,
title = {{NeMa: Fast Graph Search with Label Similarity}},
author = {Khan, Arijit and Wu, Yinghui and Aggarwal, Charu C. and Yan, Xifeng},
journal = {PVLDB},
series = {{VLDB} '13},
volume = {6},
number = {3},
pages = {181--192},
doi = {10.14778/2535568.2535571},
url = {https://doi.org/10.14778/2535568.2535571},
year = {2013}
}Incoming Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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