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LEARNING LATENT COMMUNITY STRUCTURES IN NETWORK-BASED DATA
[thesis]
2020
In this thesis we study two models that incorporate latent group structure related to networks. In particular, for the first part we introduce a new multitype recursive tree model called Community Modulated Recursive Tree (CMRT) that assigns group labels to vertices in a way similar to the popular stochastic block model for random graphs. Then we introduce a closely related population dependent branching process, and proceed to derive some of CMRT's asymptotic properties based on that,
doi:10.17615/8k6q-hj51
fatcat:vwyscw5vnje6xnbqp6bexr2up4