Predicting Links in Human Contact Networks Using Online Social Proximity

  • Annalisa SocievoleEmail author
  • Floriano De Rango
  • Salvatore Marano
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


Experimentally measured contact traces, such as those obtained through short range wireless sensors, have allowed researchers to study how mobile users contact each other in different environments. These traces often include other types of useful information such as users’ social profiles and their online friend lists. This explicit social information is important since it can be exploited for augmenting the knowledge of user behavior and hence improve the quality of human mobility analysis. In this paper, we use online social ties for predicting users’ contacts. Specifically, we study the prediction of links in human contact networks as a graph inference problem, where the existence of an edge is predicted using different proximity measures that quantify the closeness or similarity between nodes. First, we predict the edges of the contact graph when we have only information about users’ online social network. Next, we analyze the effectiveness of using both the online social network and a part of the contact network for contact prediction. In both settings, our study on three different human contact traces shows that resource allocation measure plays a significant role in contact prediction. Furthermore, the results demonstrate the importance of online social proximity in identifying stronger ties.


Link predictionBehavioral predictionContact networksTie prediction 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Annalisa Socievole
    • 1
    Email author
  • Floriano De Rango
    • 1
  • Salvatore Marano
    • 1
  1. 1.Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica (DIMES)Università della CalabriaRendeItalia

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