6 papers · 1 filter
Minimax Synthesis of Network Mechanisms
Marios Papamichalis, Regina Ruane
A single observed network reflects several mechanisms at once: communities, hubs, and clustering coexist in one graph, each a different model. We treat the network as a combination…
Collapsed Structured Block Models for Community Detection in Complex Networks
Marios Papamichalis, Regina Ruane
Community detection seeks to recover mesoscopic structure from network data that may be binary, count-valued, signed, directed, weighted, or multilayer. The stochastic block model…
Decision-Theoretic Robustness for Network Models
Marios Papamichalis, Regina Ruane, Simon Lunagomez +1
Bayesian network models (Erdos Renyi, stochastic block models, random dot product graphs, graphons) are widely used in neuroscience, epidemiology, and the social sciences, yet real…
Decomposing Degree Assortativity in Sparse Spatial Networks
Marios Papamichalis, Regina Ruane
Spatial networks are typically assortative: well-connected nodes link to other well-connected nodes, and the usual reading is sorting, popular nodes seeking each other out. In spac…
Wavelet Latent Position Exponential Random Graphs
Marios Papamichalis, Regina Ruane
Many network datasets exhibit connectivity with variance by resolution and large-scale organization that coexists with localized departures. When vertices have observed ordering or…
Graphon-Level Bayesian Predictive Synthesis for Random Network
Marios Papamichalis, Regina Ruane
Bayesian predictive synthesis provides a coherent Bayesian framework for combining multiple predictive distributions, or agents, into a single updated prediction, extending Bayesia…