Truss Decomposition in Massive Networks
Summary: Truss decomposition for massive networks; the paper uses k-truss as a polynomial-time, informative core of the k-core. It strengthens the in-memory algorithm and introduces two I/O-efficient methods for out-of-core graphs, with real-data experiments showing scalability and the value of k-truss. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jia Wang (Chinese University of Hong Kong)
- 2. James Cheng (Nanyang Technological University)
BibTeX Citation
@article{wang_vldb12,
title = {{Truss Decomposition in Massive Networks}},
author = {Wang, Jia and Cheng, James},
journal = {PVLDB},
series = {{VLDB} '12},
volume = {5},
number = {9},
pages = {812--823},
doi = {10.14778/2315356.2315361},
url = {https://doi.org/10.14778/2315356.2315361},
year = {2012}
}
Incoming Citations (Sorted by Pagerank)
Showing 18 of 68 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 262 | On Triangulation-based Dense Neighborhood Graph Discovery | 2011 | VLDB | 0.00023084332 |
| 674 | Finding Maximal Cliques in Massive Networks by H*-graph | 2010 | SIGMOD | 0.00015081844 |
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