The Local Closure Coefficient: A New Perspective On Network Clustering

Abstract

The phenomenon of edge clustering in real-world networks is a fundamental property underlying many ideas and techniques in network science. Clustering is typically quantified by the clustering coefficient, which measures the fraction of pairs of neighbors of a given center node that are connected. However, many common explanations of edge clustering attribute the triadic closure to a head node instead of the center node of a length-2 path; for example, a friend of my friend is also my friend. While such explanations are common in network analysis, there is no measurement for edge clustering that can be attributed to the head node. Here we develop local closure coefficients as a metric quantifying head-node-based edge clustering. We define the local closure coefficient as the fraction of length-2 paths emanating from the head node that induce a triangle. This subtle difference in definition leads to remarkably different properties from traditional clustering coefficients. We analyze correlations with node degree, connect the closure coefficient to community detection, and show that closure coefficients as a feature can improve link prediction.