network-modeling

Heider vs Simmel: Emergent Features in Dynamic Structures

This is a paper as part of the reviewed proceedings of the ICML 2006 Workshop on Statistical Network Analysis, entitled 'Statistical Network Analysis: Models, Issues, and New Directions' published in the Lecture Notes in Computer Science Series. …

Statistical Network Analysis: Models, Issues and New Directions: Panel Discussion

This is a discussion of the ICML 2006 Workshop on Statistical Network Analysis, entitled 'Statistical Network Analysis: Models, Issues, and New Directions' published in the Lecture Notes in Computer Science Series.

Degree distributions in sexual networks: A framework for evaluating evidence

Objective: We present a likelihood based statistical framework to test the fit of power-law and alternative social process models for the degree distribution, and derive the sexually transmitted infection epidemic predictions from each model. Study …

Model-based Assessment of the Impact of Missing Data on Inference for Networks

Most inference using social network models assumes that the presence or absence of all relations is known. This is rarely the case. Most social network analysis ignores the problem of missing data by including only actors with complete observations. …

Inference in curved exponential family models for networks

Network data arise in a wide variety of applications. Although descriptive statistics for networks abound in the literature, the science of fitting statistical models to complex network data is still in its infancy. The models considered in this …

Interval Estimates for Epidemic Thresholds in Two-Sex Network Models

Epidemic thresholds in network models of heterogeneous populations characterized by highly right-skewed contact distributions can be very small. When the population is above the threshold, an epidemic is inevitable and conventional control measures …

Positional Estimation Within a Latent Space Model for Networks

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently …

New specifications for exponential random graph models

The most promising class of statistical models for expressing structural properties of social networks observed at one moment in time is the class of exponential random graph models (ERGMs), also known as p* models. The strong point of these models …

Model-Based Clustering for Social Networks

Network models are widely used to represent relations among interacting units or actors. Network data often exhibit transitivity, meaning that two actors that have ties to a third actor are more likely to be tied than actors that do not, homophily by …

Inference in curved exponential family models for networks

Network data arise in a wide variety of applications. Although descriptive statistics for networks abound in the literature, the science of fitting statistical models to complex network data is still in its infancy. The models considered in this …