The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Finding community structure in mega-scale social networks: [extended abstract]
Open accessAbstract
Community analysis algorithm proposed by Clauset, Newman, and Moore (CNM algorithm) finds community structure in social networks. Unfortunately, CNM algorithm does not scale well and its use is practically limited to networks whose sizes are up to 500,000 nodes. We show that this inefficiency is caused from merging communities in unbalanced manner and that a simple heuristics that attempts to merge community structures in a balanced manner can dramatically improve community structure analysis. The proposed techniques are tested using data sets obtained from existing social networking service that hosts 5.5 million users. We have tested three three variations of the heuristics. The fastest method processes a SNS friendship network with 1 million users in 5 minutes (70 times faster than CNM) and another friendship network with 4 million users in 35 minutes, respectively. Another one processes a network with 500,000 nodes in 50 minutes (7 times faster than CNM), finds community structures that has improved modularity, and scales to a network with 5.5 million.
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Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Finding community structure in very large networks
Clauset, A., Newman, M. E. J., & Moore, C. (2004) · Physical Review E · doi:10.1103/physreve.70.066111 · Cited by 7,390
Defining and identifying communities in networks
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400054101 · Cited by 2,450
Cited by
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
A survey of heterogeneous information network analysis
Shi, C., Li, Y., Zhang, J., Sun, Y., & Yu, P. S. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2598561 · Cited by 1,112
Mapping Change in Large Networks
Rosvall, M., & Bergstrom, C. T. (2010) · PLoS ONE · doi:10.1371/journal.pone.0008694 · Cited by 630
Himelboim, I., McCreery, S., & Smith, M. (2013) · Journal of Computer-Mediated Communication · doi:10.1111/jcc4.12001 · Cited by 511
Rosvall, M., & Bergstrom, C. T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018209 · Cited by 393
Metadata
- Authors
- Wakita, K., & Tsurumi, T.
- Venue
- Proceedings of the 16th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2007-05-08
- Pages
- 1275-1276
- OpenAlex
- W2134931490
- Cited by
- 396
- References
- 4
- FWCI
- 18.26
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wakita, K., & Tsurumi, T. (2007). Finding community structure in mega-scale social networks: [extended abstract]. Proceedings of the 16th international conference on World Wide Web. https://doi.org/10.1145/1242572.1242805
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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