Real-world networks are rarely static. Recently, there has been increasing interest in both network growth and network densification, in which the number of edges scales superlinearly with the number of nodes. Less studied but equally important, however, are scaling laws of higher-order cliques, which can drive clustering and network redundancy. In this paper, we study how cliques grow with network size, by analyzing several empirical networks from emails to Wikipedia interactions. Our results show superlinear scaling laws whose exponents increase with clique size, in contrast to predictions from a previous model. We then show that these results are in qualitative agreement with a model that we propose, the local preferential attachment model, where an incoming node links not only to a target node, but also to its higher-degree neighbors. Our results provide insights into how networks grow and where network redundancy occurs.

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• Classical statistical mechanics
• Growth processes
• Stochastic analysis methods
• Time series analysis