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DISCCO: Distance-Based Bayesian Spatial Clustering of Complex Objects with Node-Frailty Centrality
Clustering problems increasingly involve complex objects observed over space, such as distributions, matrices, functions, images, or multivariate data, for which a scientifically meaningful dissimilarity between objects is often easier to specify and computationally more tractable than an object-response likelihood mod...
On Graph-Informed Distance Metrics for Comparing Graph Partitions
Under stochastic block models, it is proved that stronger topological disruptions incur asymptotically larger distances almost surely in both inter-community and intra-community split settings.