The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Graph Wavelets for Multiscale Community Mining
Abstract
We develop a signal processing approach to the multiscale detection of communities in networks, that is of groups of nodes well connected together. The method relies on carefully engineered wavelets on graphs to introduce the notion of scale and to obtain a local view of the graph from each node. Computing the correlations between wavelets centered at different nodes, one has access to a notion of similarity between nodes, thereby enabling a clustering procedure that groups nodes according to their community at the scale of analysis. By using a collection of random vectors to estimate the correlation between the nodes, we show that the method is suitable for the analysis of large graphs. Furthermore, we introduce a notion of partition stability and a statistical test allowing us to assess which scales of analysis of the network are relevant. The effectiveness of the method is discussed first on multiscale graph benchmarks, then on real data of social networks and on models for signal processing on graphs.
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Cites
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
Gephi: An Open Source Software for Exploring and Manipulating Networks
Bastian, M., Heymann, S., & Jacomy, M. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13937 · Cited by 11,675
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Tibshirani, R., Walther, G., & Hastie, T. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00293 · Cited by 6,205
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224
Resolution limit in community detection
Fortunato, S., & Barthélemy, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605965104 · Cited by 3,024
Discrete Signal Processing on Graphs
Sandryhaila, A., & Moura, J. M. F. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2238935 · Cited by 1,531
Cited by
Discrete Signal Processing on Graphs: Sampling Theory
Chen, S., Varma, R., Sandryhaila, A., & Kovacevic, J. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2469645 · Cited by 666
Random walks and diffusion on networks
Masuda, N., Porter, M. A., & Lambiotte, R. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.07.007 · Cited by 657
Learning Laplacian Matrix in Smooth Graph Signal Representations
Dong, X., Thanou, D., Frossard, P., & Vandergheynst, P. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2602809 · Cited by 640
Metadata
- Authors
- Tremblay, N., & Borgnat, P.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2014-10
- Volume
- 62
- Issue
- 20
- Pages
- 5227-5239
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2021969242
- Cited by
- 170
- References
- 8
- FWCI
- 16.51
- Type
- journal article
- Language
- en
- Open access
- closed
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