Betweenness Preference: Quantifying Correlations in the Topological Dynamics of Temporal Networks

We study correlations in temporal networks and introduce the notion of betweenness preference. It allows us to quantify to what extent paths, existing in time-aggregated representations of temporal networks, are actually realizable based on the sequence of interactions. We show that betweenness preference is present in empirical temporal network data and that it influences the length of the shortest time-respecting paths. Using four different data sets, we further argue that neglecting betweenness preference leads to wrong conclusions about dynamical processes on temporal networks.

[1]  Rossano Schifanella,et al.  On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks , 2011, Ad Hoc Networks.

[2]  Jari Saramäki,et al.  Temporal motifs in time-dependent networks , 2011, ArXiv.

[3]  Ross J. Anderson,et al.  Temporal node centrality in complex networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Dami'an H. Zanette,et al.  Synchronised firing induced by network dynamics in excitable systems , 2012, 1206.6975.

[5]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[6]  Esteban Moro Egido,et al.  The dynamical strength of social ties in information spreading , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Alessandro Vespignani,et al.  Random walks and search in time-varying networks. , 2012, Physical review letters.

[8]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Petter Holme,et al.  Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts , 2010, PLoS Comput. Biol..

[10]  Jari Saramäki,et al.  Small But Slow World: How Network Topology and Burstiness Slow Down Spreading , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  R. Pastor-Satorras,et al.  Activity driven modeling of time varying networks , 2012, Scientific Reports.

[12]  Romualdo Pastor-Satorras,et al.  Random walks on temporal networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[14]  H. Stanley,et al.  Dynamic networks and directed percolation , 2009, 0901.4563.

[15]  Jari Saramäki,et al.  Temporal Networks , 2011, Encyclopedia of Social Network Analysis and Mining.

[16]  Esteban Moro,et al.  Impact of human activity patterns on the dynamics of information diffusion. , 2009, Physical review letters.

[17]  Qi He,et al.  Communication motifs: a tool to characterize social communications , 2010, CIKM.

[18]  Jari Saramäki,et al.  Path lengths, correlations, and centrality in temporal networks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Bivas Mitra,et al.  Intrinsically dynamic network communities , 2011, Comput. Networks.

[20]  Ciro Cattuto,et al.  What's in a crowd? Analysis of face-to-face behavioral networks , 2010, Journal of theoretical biology.

[21]  Anna Dornhaus,et al.  Time-Ordered Networks Reveal Limitations to Information Flow in Ant Colonies , 2011, PloS one.