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 3 of 53 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,203 | cuMatch: A GPU-based Memory-Efficient Worst-case Optimal Join Processing Method for Subgraph Queries with Complex Patterns | 2025 | SIGMOD | 4.9793485e-05 |
| 11,286 | Efficient and Accurate Subgraph Counting: A Bottom-up Flow-learning Based Approach | 2025 | VLDB | 4.9793485e-05 |
| 11,535 | Atom: An Efficient Query Serving System for Embedding-based Knowledge Graph Reasoning with Operator-level Batching | 2024 | SIGMOD | 4.9793485e-05 |
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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