Interest clustering coefficient: a new metric for directed networks like Twitter

DOI10.1093/COMNET/CNAB030zbMATH Open1483.90038arXiv2008.00517OpenAlexW4225506584MaRDI QIDQ5861617

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Publication date: 1 March 2022

Published in: Journal of Complex Networks (Search for Journal in Brave)

Abstract: We study here the clustering of directed social graphs. The clustering coefficient has been introduced to capture the social phenomena that a friend of a friend tends to be my friend. This metric has been widely studied and has shown to be of great interest to describe the characteristics of a social graph. In fact, the clustering coefficient is adapted for a graph in which the links are undirected, such as friendship links (Facebook) or professional links (LinkedIn). For a graph in which links are directed from a source of information to a consumer of information, it is no more adequate. We show that former studies have missed much of the information contained in the directed part of such graphs. We thus introduce a new metric to measure the clustering of a directed social graph with interest links, namely the interest clustering coefficient. We compute it (exactly and using sampling methods) on a very large social graph, a Twitter snapshot with 505 million users and 23 billion links. We additionally provide the values of the formerly introduced directed and undirected metrics, a first on such a large snapshot. We exhibit that the interest clustering coefficient is larger than classic directed clustering coefficients introduced in the literature. This shows the relevancy of the metric to capture the informational aspects of directed graphs.

Full work available at URL: https://arxiv.org/abs/2008.00517

zbMATH Keywords

social networks complex networks clustering coefficient twitter directed networks link recommendation

Mathematics Subject Classification ID

Social networks; opinion dynamics (91D30) Deterministic network models in operations research (90B10)