Abstract
Given a large and complex network, we would like to find the partition of this network into a small number of clusters. This question has been addressed in many different ways. In a previous paper, we proposed a deterministic framework for an optimal partition of a network as well as the associated algorithms. In this paper, we extend this framework to a probabilistic setting, in which each node has a certain probability of belonging to a certain cluster. Two classes of numerical algorithms for such a probabilistic network partition are presented and tested. Application to three representative examples is discussed.
Original language | English (US) |
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Article number | 026106 |
Journal | Physical Review E - Statistical, Nonlinear, and Soft Matter Physics |
Volume | 80 |
Issue number | 2 |
DOIs | |
State | Published - Aug 7 2009 |
All Science Journal Classification (ASJC) codes
- Condensed Matter Physics
- Statistical and Nonlinear Physics
- Statistics and Probability