Network community detection is a task of dividing a set of network’s nodes into groups, such that intra-group
connections are more dense than inter-group connections. We consider a specific type of clustering, so-called multi-view
clustering, which deal with a set of graphs defined on the same set of nodes, but different edges. The goal is to divide
nodes into subgroups taking into account all graphs. We propose an ensemble method for multi-view clustering, applicable
to any greedy algorithm with nodes traversal. Instead of traversing nodes of the graphs individually, our approach does
a co-clustering of all networks’ nodes. Decision about node color takes into account connections in all input graphs. We
demonstrate the performance of our method, applied on a popular Louvain modularity algorithm, using real dataset and a
synthetic dataset (with known clustering structure).
Key words:
community detection, multi-view clustering, ensemble methods, Louvain modularity
DOI: 10.31857/S0235009221010029
Cite:
Baikov I. I., Semerova E. A., Kurmukov A. I.
Metod ansamblirovaniya algoritmov klasterizatsii dlya resheniya zadachi sovmestnoi klasterizatsii
[General ensemble method for multi-view clustering].
Sensornye sistemy [Sensory systems].
2021.
V. 35(1).
P. 43–49 (in Russian). doi: 10.31857/S0235009221010029
References:
- Blondel V.D., Guillaume J.L., Lambiotte R., Lefebvre E. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment. 2008. V. 10. DOI: P10008.
- Bickel S., Scheffer T. Multi-view clustering. ICDM. 2004. V. 4. P. 19–26.
- Chao G., Sun S., Bi J. A survey on multi-view clustering. URL: https://arxiv.org/abs/1712.06246. 2017.
- Hubert L., Arabie P. Comparing partitions. Journal of clasification. 1985. V. 2(1). P. 193–218.
- Kurmukov A., Dodonova Y., Zhukov L. Classification of normal and pathological brain networks based on similarity in graph partitions. 16th International Conference on Data Mining Workshops (ICDMW). 2016. P. 107–112.
- Kurmukov A., Mussabaeva A., Denisova Y., Moyer D., Jhanshad N., Thompson P.M., Gutman B.A. Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering. Brain Connectivity. 2020. V. 10(4). P. 183–194.
- Lancichinetti A., Fortunato S. Consensus clustering in complex networks. Scientific reports. 2012. V. 2. P. 336.
- Newman M.E. Mixing patterns in networks. Physical Review. 2003. V. 67. DOI: 026126
- Newman M.E. Modularity and community structure in networks. Proceedings of the National Academy of Sciences. 2006. V. 103 (23). P. 8577–8582.
- Raghavan U.N., Albert R., Kumara S. Near linear time algorithm to detect community structures in large-scale networks. Physical Review. 2007. V. 76 (3). P. 036106.
- Desikan R.S., Se ́gonne F., Fischl B., Quinn B.T., Dickerson B.C., Blacker D., Buckner R.L., Dale A.M., Maguire R.P., Hyman B.T., Albert M.S., Killiany R.J. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006. V. 31. P. 968–980.
- Strehl A., Ghosh J. Cluster ensembles – a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research. 2002. V. 3. P. 583–617.
- Mueller S.G., Weiner M.W., Thal L.J., Petersen R.C., Jack C.R., Jagust W., Beckett L. Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s & Dementia. 2005. V. 1 (1). P. 55–66.
- Traag V.A., Waltman L., van Eck N.J. From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports. 2019. V. 9 (1). P. 1–12.
- Yang Y., Wang H. Multi-view clustering: A survey. Big Data Mining and Analytics. 2018. V. 1 (2). P. 83–107.
