A graph model with indirect co-location links

Md Shahzamal, Raja Jurdak, Bernard Mans, Frank De Hoog

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of Workshop on Mining and Learning with Graphs (MLG’2018)
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages8
Publication statusPublished - 20 Aug 2018
Event14th International Workshop on Mining and Learning with Graphs (MLG 2018) - London, United Kingdom
Duration: 20 Aug 201820 Aug 2018


Conference14th International Workshop on Mining and Learning with Graphs (MLG 2018)
Country/TerritoryUnited Kingdom


  • Dynamic graphs
  • graph mining
  • social networks
  • disease tracking

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