Figure 2

Degree distributions in mobile phone networks. The degree distributions of several datasets have comparable features, but differences in the construction, the time range of the dataset and the size of the system lead to different shapes. Note the bump in (d), when non-reciprocal links are taken into account. (a) Aiello, W. et al., ‘A random graph model for massive graphs’, in: Proceedings of the thirty-second annual ACM symposium on theory of computing, pp 171-180 [12] ©2000 Association for Computing Machinery, Inc. Reprinted by permission. http://doi.acm.org/10.1145/335305.335326. (b) Nanavati, A.A. et al., ‘On the structural properties of massive telecom call graphs: findings and implications’, in: Proceedings of the 15th ACM international conference on information and knowledge management, pp 435-444 [19] ©2006 Association for Computing Machinery, Inc. Reprinted by permission. http://doi.acm.org/10.1145/1183614.1183678. (c) Seshadri, M. et al., ‘Mobile call graphs: beyond power-law and lognormal distributions.’ in: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 596-604 [23] ©2008 Association for Computing Machinery, Inc. Reprinted by permission. http://doi.acm.org/10.1145/1401890.1401963. (d) Figure reproduced from [25].