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Techniques for clustering interaction data as a collection of graphs
A natural approach to analyze interaction data of form "what-connects-to-what-when" is to create a time-series (or rather a sequence) of graphs through temporal discretization (bandwidth selection) and spatial discretization (vertex contraction).
Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs
We consider a problem of grouping multiple graphs into several clusters using singular value thesholding and non-negative factorization.
A model selection approach for clustering a multinomial sequence with non-negative factorization
We consider a problem of clustering a sequence of multinomial observations by way of a model selection criterion.
On latent position inference from doubly stochastic messaging activities
Each of the message-exchanging actors is modeled as a process in a latent space.
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