social network analysis

Practical Network Modeling via Tapered Exponential-family Random Graph Models

In this talk we discuss a new class of models for social networks that which circumvents the issue of near-degeneracy while maintaining the desirable features of exponential-family models.

Practical Network Modeling via Tapered Exponential-family Random Graph Models

In this talk we discuss a new class of models for social networks that which circumvents the issue of near-degeneracy while maintaining the desirable features of exponential-family models.

Practical Network Modeling via Tapered Exponential-family Random Graph Models

Exponential-family Random Graph Models (ERGMs) have long been at the forefront of the analysis of relational data. The exponential-family form allows complex network dependencies to be represented. Models in this class are interpretable, flexible and …

Comparing the real-world performance of exponential-family random graph models and latent order logistic models for social network analysis

In this talk we assess the real-world performance of Latent Order Logistic models (LOLOG).

Comparing the real-world performance of exponential-family random graph models and latent order logistic models for social network analysis

We assess the real-world performance of Latent Order Logistic models (LOLOG) when applied to typical networks modelled by researchers by comparing them to Exponential-family random graph models (ERGMs). We demonstrate that the LOLOG models are, in …

Exponential-family Random Network Models

Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves to be a useful class of models for representing …

Fitting Position Latent Cluster Models for Social Networks with latentnet

latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002) suggested an approach to modeling networks based on positing the existence of an latent space of …

networksis: A Package to Simulate Bipartite Graphs with Fixed Marginals through Sequential Importance Sampling

The ability to simulate graphs with given properties is important for the analysis of social networks. Sequential importance sampling has been shown to be particularly effective in estimating the number of graphs adhering to fixed marginals and in …