Computational Modeling of Spatio-temporal Social Networks: A Time-Aggregated Graph Approach

Spatio-Temporal Constraints on Social Networks Workshop, University of California, Santa Barbara, Center for Spatial Studies, 13-14 December 2010

Shashi Shekhar and Dev Oliver

“Social computing is transforming on-line spaces with popular applications such as social networking (e.g., Facebook), collaborative authoring (e.g., Wikipedia), social bargain hunting (e.g., Groupon), etc. Spatio-temporal constraints are becoming a critical issue in social computing with the emergence of location-based social-networking, Volunteered
Geographic Information (Goo 07, Elw 08), Participative Planning (Elw 08, Fis 01), etc. Location-based social networks (e.g., and the “Places Check-in” feature on Facebook) facilitate socialization with nearby friends at restaurants, bars, museums, and concerts. Volunteered Geographic Information (e.g., Wikimapia, OpenStreetMap, Google MyMaps) allows Internet users to participate in generation of geographic information. Traditional computational models for social networks are based on graphs [Fre 06, Was 94, Nrc 03, Cro 09], where nodes represent individual actors (e.g., persons, organizations) and edges represent relationship ties (e.g., communication, financial aid, contracts) between actors. Such graph models are used to assess centrality and the influence of actors (e.g., measures such as degree, reach, “between-ness,” bridge), as well as community structure (e.g., measures such as cohesion, clustering, etc.). Statistical properties such as skewed degree distribution are modeled by random graphs [New 02, Nrc 03], where each node-pair has a connecting edge with independent probability p, which may depend on factors such as geographic distance [Won 05].

“However, traditional graph and random graph models are limited in addressing spatio-temporal questions such as change (e.g., how is trust or leadership changing over time? who are the emerging leaders in a group? what are the recurring changes in a group?), trends (e.g., what are the long-term and short-term trends in network size or structure? what are the exceptions to the long-term trend?), duration (e.g., how long is the tenure of a leader in a group? how long does it take to elevate the level of trust such as a relationship changing from visitor to friend?), migration, mobility and travel (e.g., interplay between travel behavior and size/structure of social networks [Tim 06]). This position paper explores time-aggregated graph models to support computational tools to address such questions.”

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