Analysis of a large-scale weighted network of one-to-one human communication

Jukka-Pekka Onnela, Helsinki University of Technology and Oxford University
Jari Saramäki, Helsinki University of Technology
Jörkki Hyvönen, Helsinki University of Technology
Gábor Szabó, University of Notre Dame and Harvard University
M. Argollo de Menezes, University of Notre Dame
Kimmo Kaski, Helsinki University of Technology
Albert-László Barabási, University of Notre Dame and Harvard University
János Kertész, Helsinki University of Technology and Budapest University of Technology and Economics

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Originally published in New Journal of Physics 9(June), 2007. doi:10.1088/1367-2630/9/6/179

Abstract

We construct a connected network of 3.9 million nodes from mobile phone call records, which can be regarded as a proxy for the underlying human communication network at the societal level. We assign two weights on each edge to reflect the strength of social interaction, which are the aggregate call duration and the cumulative number of calls placed between the individuals over a period of 18 weeks. We present a detailed analysis of this weighted network by examining its degree, strength, and weight distributions, as well as its topological assortativity and weighted assortativity, clustering and weighted clustering, together with correlations between these quantities.We give an account of motif intensity and coherence distributions and compare them to a randomized reference system.We also use the concept of link overlap to measure the number of common neighbours any two adjacent nodes have, which serves as a useful local measure for identifying the interconnectedness of communities. We report a positive correlation between the overlap and weight of a link, thus providing strong quantitative evidence for the weak ties hypothesis, a central concept in social network analysis. The percolation properties of the network are found to depend on the type and order of removed links, and they can help understand how the local structure of the network manifests itself at the global level.We hope that our results will contribute to modelling weighted large-scale social networks, and believe that the systematic approach followed here can be adopted to study other weighted networks.