School of Electronic Engineering and Computer Science

Cecilia Mascolo, Computer Laboratory, Cambridge

7 November 2012

Time: 3:00 - 4:00pm
Venue: BR 3.02 Bancroft Road Teaching Rooms Peter Landin Building London E1 4NS

Space, Time and Social Ties: How Geographic Distance Shapes Online Social Networks

Space, Time and Social Ties: How Geographic Distance Shapes
Online Social Networks

While in the last years massive online social networks have become extremely popular, gathering and engaging millions of users, only
recently these social services are becoming location-aware. This provides broad and fine grained data to investigate how spatial and social structure blend together, opening exciting research directions with promising scientific and practical applications. For instance, an open question about human social behavior is to understand whether and how, spatial distance between two individuals affects their social interaction. In this talk we will present how the socio-spatial properties of online social networks can be studied and how social and spatial properties can be jointly exploited to build new systems and applications. We discuss a comprehensive analysis of the spatial properties of the social networks arising among users of popular online location-based services. We observe robust universal features across them: there is strong heterogeneity across users, with different characteristic spatial lengths of interaction across both their social ties and social triads. We extend these results with a
detailed study of the temporal evolution of a social network with spatial information: since node degree and spatial distance simultaneously shape user connections, we describe and evaluate a gravitational model of network growth which is able to capture the social and spatial properties observed in real networks, confirming our findings. There a number of possible application of geo-social models for social networks and we illustrate the exploitation of our research in the design of a link prediction system for online social networks based on the places that users visit as well as a content cascade geographical spread prediction model which improves content caching on content delivery networks.

Biography: Dr. Cecilia Mascolo is a Reader in Mobile Systems in the Computer Laboratory, University of Cambridge, UK. Prior to this, she was with the Department of Computer Science of University College London, UK. She holds an MSc and PhD in Computer Science from University of Bologna (Italy). Cecilia’s research concentrates on mobility and social data gathering, analysis, modeling and exploitation through research council and industry funded projects. Most of the projects are multi-disciplinary. Her research strategy is heavily experimental and deployment oriented. She has published extensively in the areas of mobile sensor networks, mobile network routing, realistic mobility models and social network analysis. Cecilia has served as in the Organization Committees of many mobile and sensor systems, social network, middleware, software engineering and data mining conferences and workshops. She is on the editorial board of IEEE Transactions on Mobile Computing, IEEE Internet Computing and IEEE Pervasive Computing.