The critical point of k-clique percolation in the Erdos-Renyi graph
classification
❄️ cond-mat.dis-nn
cond-mat.stat-mech
keywords
k-cliquepercolationsizeaveragecriticalerdos-renyigraphprobability
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Motivated by the success of a k-clique percolation method for the identification of overlapping communities in large real networks, here we study the k-clique percolation problem in the Erdos-Renyi graph. When the probability p of two nodes being connected is above a certain threshold p_c(k), the complete subgraphs of size k (the k-cliques) are organized into a giant cluster. By making some assumptions that are expected to be valid below the threshold, we determine the average size of the k-clique percolation clusters, using a generating function formalism. From the divergence of this average size we then derive an analytic expression for the critical linking probability p_c(k).
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