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Proceedings of the International Conference on Social Modeling and Simulation, plus Econophysics Colloquium 2014
Graph partitioning, or community detection, is an important tool for investigating the structures embedded in real data. The spectral method is a major algorithm for graph partitioning and is also analytically tractable. In order to analyze the performance of the spectral method, we consider a regular graph of two loosely connected clusters, each of which consists of a random graph, i.e., a random graph with a planted partition. Since we focus on the bisection of regular random graphs, whetherdoi:10.1007/978-3-319-20591-5_12 fatcat:jcnjkeamfrcktm3utdpkg5e7me