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Community Detection in Social Networks: An In-depth Benchmarking Study with a Procedure-Oriented Framework

Summary: Community detection benchmarking in social networks using a generalized procedure-oriented framework. Re-implements ten state-of-the-art algorithms under identical conditions, enabling thorough, fair comparisons and systematic diagnosis of weaknesses and improvements. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11398
Venue
VLDB
Year
2015
Pagerank
8.3887788e-05
Overall Rank
2,580 | 82.31%
DOI
10.14778/2794367.2794370

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wang_vldb15,
        title = {{Community Detection in Social Networks: An In-depth Benchmarking Study with a Procedure-Oriented Framework}},
        author = {Wang, Meng and Wang, Chaokun and Yu, Jeffrey Xu and Zhang, Jun},
        journal = {PVLDB},
        series = {{VLDB} '15},
        volume = {8},
        number = {10},
        pages = {998--1009},
        doi = {10.14778/2794367.2794370},
        url = {https://doi.org/10.14778/2794367.2794370},
        year = {2015}
}

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.

Rank Cited Paper Year Venue Pagerank
138 Discovering Large Dense Subgraphs in Massive Graphs 2005 VLDB 0.00029823423
590 Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis 2013 VLDB 0.0001605572
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