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…
Evidence thresholds of collapsed structured block models
Marios Papamichalis, Regina Ruane
A stochastic block model explains a network by splitting its nodes into communities and a matrix of tie probabilities between them. Collapsing those probabilities out scores each s…
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…
Multiscale Localized Inference for Networks with a Measured Vertex Coordinate
Marios Papamichalis, Regina Ruane
In many networks each vertex has a position measured from outside the network: a neuron's location along the body axis, a residue's index along a protein sequence, a genomic bin's…
Graphon-Level Bayesian Predictive Synthesis for Random Network
Marios Papamichalis, Regina Ruane
Analysts often fit several models to the same network. Each estimates a graphon, the function giving the probability of a link between two nodes. Reporting one predictive graphon m…
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…