Community Detection for Big Data Networks
Raghvendra Mall and Johan A.K. Suykens
In this chapter, we demonstrate the applicability of the kernel spectral clustering (KSC) method for community detection in Big Data networks. We give a practical exposition of the KSC method  on large-scale synthetic and real-world networks with up to 106 nodes and 107 edges. The KSC method uses a primal–dual framework to construct a model on a smaller subset of the Big Data network. The original large-scale kernel matrix cannot fit in memory. So we select smaller subgraphs using a fast and unique representative subset (FURS) selection technique as proposed in Reference 2. These subsets are used for training and validation, respectively, ...