CECI: Compact Embedding Cluster Index for Scalable Subgraph Matching
Summary: CECI: Compact Embedding Cluster Index for scalable subgraph matching; partitions graph into embedding clusters. BFS pruning with rev-BFS refinement prune candidates; set-intersection replaces edge checks; cost-estimation guides clustering; 20.4x faster listing embeddings, 2.6x for 1,024. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Bibek Bhattarai (George Washington University)
- 2. Hang Liu (George Washington University; University of Massachusetts Lowell)
- 3. H. Howie Huang (George Washington University)
BibTeX Citation
@inproceedings{bhattarai_sigmod19,
title = {{CECI: Compact Embedding Cluster Index for Scalable Subgraph Matching}},
author = {Bhattarai, Bibek and Liu, Hang and Huang, H. Howie},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3300086},
url = {https://dl.acm.org/doi/10.1145/3299869.3300086},
year = {2019}
}
Incoming Citations (Sorted by Pagerank)
Showing 50 of 54 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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