234 citations · 1.2k across the 33 of their papers we have counts for
14 papers · 1 filter
Distributed estimation through parallel approximants
Aritra Chakravorty, William S. Cleveland, Patrick J. Wolfe
Designing scalable estimation algorithms is a core challenge in modern statistics. Here we introduce a framework to address this challenge based on parallel approximants, which yie…
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…
Modeling Network Populations via Graph Distances
Simón Lunagómez, Sofia C. Olhede, Patrick J. Wolfe
This article introduces a new class of models for multiple networks. The core idea is to parametrize a distribution on labelled graphs in terms of a Fréchet mean graph (which depen…
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…
Topology reveals universal features for network comparison
Pierre-André G. Maugis, Sofia C. Olhede, Patrick J. Wolfe
The topology of any complex system is key to understanding its structure and function. Fundamentally, algebraic topology guarantees that any system represented by a network can be…
Statistical inference for network samples using subgraph counts
P-A. G. Maugis, Carey E. Priebe, S. C. Olhede +1
We consider that a network is an observation, and a collection of observed networks forms a sample. In this setting, we provide methods to test whether all observations in a networ…