HiNGE: Enabling Temporal Network Analytics at Scale
Summary: HiNGE enables scalable temporal network analytics via a distributed graph DB storing full history and enabling past-snapshot queries. DeltaGraph, a hierarchical index, records historical traces on disk; GraphPool keeps hundreds of snapshots in memory for fast parallel analytics. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Udayan Khurana (University of Maryland)
- 2. Amol Deshpande (University of Maryland)
BibTeX Citation
@inproceedings{khurana_sigmod13,
title = {{HiNGE: Enabling Temporal Network Analytics at Scale}},
author = {Khurana, Udayan and Deshpande, Amol},
series = {{SIGMOD} '13},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2463676.2465262},
url = {https://dl.acm.org/doi/10.1145/2463676.2465262},
year = {2013}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,039 | Holistic Indexing in Main-memory Column-stores | 2015 | SIGMOD | 6.3909067e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
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