Title: A Graph Model with Indirect Co-location Links,
Abstract: Graph models are widely used to analyse diffusion processes embedded in
social contacts and to develop applications. A range of graph models are
available to replicate the underlying social structures and dynamics
realistically. However, most of the current graph models can only consider
concurrent interactions among individuals in the co-located interaction
networks. However, they do not account for indirect interactions that can
transmit spreading items to individuals who visit the same locations at
different times but within a certain time limit. The diffusion phenomena
occurring through direct and indirect interactions is called same place
different time (SPDT) diffusion. This paper introduces a model to synthesize
co-located interaction graphs capturing both direct interactions, where
individuals meet at a location, and indirect interactions, where individuals
visit the same location at different times within a set timeframe. We analyze
60 million location updates made by 2 million users from a social networking
application to characterize the graph properties, including the space-time
correlations and its time evolving characteristics, such as bursty or ongoing
behaviors. The generated synthetic graph reproduces diffusion dynamics of a
realistic contact graph, and reduces the prediction error by up to 82% when
compare to other contact graph models demonstrating its potential for
forecasting epidemic spread.