Local Graph Stability in Exponential Family Random Graph Models

Exponential random graph models

Synonyms p* models, p-star models, p1 models, exponential family of random graphs, maximum entropy random networks, logit models, Markov graphs Glossary • Graph and network: the terms are used interchangeably in this essay. • Real-world network: (real network, observed network) means network data the researcher has collected and is interested in modelling. • Ensemble of graphs: means the set of...

Learning Exponential Random Graph Models

Exponential Random Graphs are common, simple statistical models for social network and other structures. Unfortunately, inference and learning with them is hard for networks larger than 20 nodes because their partition functions are intractable to compute precisely. In this paper, we introduce a novel linear-time deterministic approximation to these partition functions. Our main insight enablin...

Exponential-Family Random Graph Models with Time Varying Network Parameters

Dynamic networks are a general language for describing time-evolving complex systems, and have long been an interesting research area. It is a fundamental research question to model time varying network parameters. However, due to difficulties in modeling functional network parameters, there is little progress in the current literature to effectively model time varying network parameters. In th...

Exponential-family random graph models for valued networks.

Exponential-family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and friend-of-a-friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMs modeling the more complex features have, to date, been limited to binary data: presence or ab...

Parametrizing Order-Stable Exponential Family Random Graph Models