Yoon-Sik Cho

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Social network data is generally incomplete with missing information about nodes and their interactions. Here we propose a spatialtemporal latent point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches, we assume that interactions are not fully observable, and certain(More)
Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both node attributes and network structure evolve under mutual influence. Specifically, we consider a mixed membership stochastic blockmodel, where the probability of(More)
With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users.(More)
BACKGROUND This research was conducted with an aim of determining the association between employment status and self-rated health. METHODS Using the data from the Third Korean Working Conditions Survey conducted in 2011, We included data from 34,783 respondents, excluding employers, self-employed workers, unpaid family workers, others. Self-rated health(More)
The emergence of location based social network (LBSN) services makes it possible to study individuals’ mobility patterns at a fine-grained level and to see how they are impacted by social factors. In this study we analyze the check-in patterns in LBSN and observe significant temporal clustering of check-in activities. We explore how self-reinforcing(More)
Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (links structure) can change with time. We propose a model of co-evolving networks where both node attributes and network structure evolve under mutual influence. Specifically, we consider a mixed membership stochastic blockmodel, where the probability of(More)
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