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Activity Number:
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124
- Recent Advances in Network Modeling and Visualizations
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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Korean International Statistical Society
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Abstract #309702
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Title:
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Modeling Interaction Lengths in Continuous-Time Dynamic Networks
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Author(s):
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Riccardo Rastelli*
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Companies:
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University College Dublin
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Keywords:
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interaction lengths;
stochastic block model;
variational inference;
integrated completed likelihood;
social network analysis
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Abstract:
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In this talk I will introduce a new stochastic block model that focuses on the analysis of interaction lengths in dynamic networks. The model does not rely on a discretization of the time dimension and may be used to analyze networks that evolve continuously over time. The framework relies on a clustering structure on the nodes, whereby two nodes belonging to the same latent group tend to create interactions and non-interactions of similar lengths. Inference is performed using a variational expectation-maximization algorithm, and a widely used clustering criterion is adopted to perform model choice. I will discuss extensions to this model that can include further dependencies on the network structure and potential covariates. I will illustrate applications of the proposed methodology to artificial data and a dataset on the London bike sharing system.
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Authors who are presenting talks have a * after their name.
