1 citations · 1 across the 23 of their papers we have counts for
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Separating Time-Varying Network Composition from Predictive Dependence under Noisy Network Measurement
Marios Papamichalis, Regina Ruane, Theofanis Papamichalis
A common question about networked time series is whether outcomes changed because shocks transmit more strongly or because the pattern of connections changed. Standard practice ins…
State-Space Modeling of Time-Varying Spillovers on Networks
Marios Papamichalis, Regina Ruane, Theofanis Papamichalis
Crime counts in city neighbourhoods, disease counts in counties, and sales at firms joined by trade are naturally represented as counts on the nodes of a network. In each case a hi…
Latent Space Network Modelling with Hyperbolic and Spherical Geometries
Marios Papamichalis, Kathryn Turnbull, Simon Lunagomez +1
A rich class of network models associate each node with a low-dimensional latent coordinate that controls the propensity for connections to form. Models of this type are well estab…
Robustness on Networks
Marios Papamichalis, Simon Lunagomez, Patrick J. Wolfe
We adopt the statistical framework on robustness proposed by Watson and Holmes in 2016 and then tackle the practical challenges that hinder its applicability to network models. The…
Evaluating and Optimizing Network Sampling Designs: Decision Theory and Information Theory Perspectives
Simón Lunagómez, Marios Papamichalis, Patrick J. Wolfe +1
Some of the most used sampling mechanisms that implicitly leverage a social network depend on tuning parameters; for instance, Respondent-Driven Sampling (RDS) is specified by the…