# A Class of Temporal Hierarchical Exponential Random Graph Models for Longitudinal Network Data

@article{Cao2017ACO, title={A Class of Temporal Hierarchical Exponential Random Graph Models for Longitudinal Network Data}, author={Ming Cao}, journal={arXiv: Methodology}, year={2017} }

As a representation of relational data over time series, longitudinal networks provide opportunities to study link formation processes. However, networks at scale often exhibits community structure (i.e. clustering), which may confound local structural effects if it is not considered appropriately in statistical analysis. To infer the (possibly) evolving clusters and other network structures (e.g. degree distribution and/or transitivity) within each community, simultaneously, we propose a class…

## References

SHOWING 1-10 OF 50 REFERENCES

Latent Space Models for Dynamic Networks

- Computer Science
- 2015

A model which embeds longitudinal network data as trajectories in a latent Euclidean space is presented and a novel approach is given to detect and visualize an attracting influence between actors using only the edge information.

Statistical clustering of temporal networks through a dynamic stochastic block model

- Computer Science
- 2015

This work explores statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of the nodes groups through time and proposes an inference procedure based on a variational expectation–maximization algorithm.

Nonparametric Multi-group Membership Model for Dynamic Networks

- Computer ScienceNIPS
- 2013

This work proposes a nonparametric multi-group membership model for dynamic networks that captures the evolution of individual node group memberships via a Factorial Hidden Markov model and explains the dynamics of the network structure by explicitly modeling the connectivity structure of groups.

New Specifications for Exponential Random Graph Models

- Computer Science, Mathematics
- 2006

It is concluded that the new specifications of exponential random graph models increase the range and applicability of the ERGM as a tool for the statistical analysis of social networks.

The statistical evaluation of social network dynamics

- Computer Science
- 2001

A class of statistical models is proposed for longitudinal network data that are continuous-time Markov chain models that can be implemented as simulation models and statistical procedures are proposed that are based on the method of moments.

Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks

- Computer ScienceAISTATS
- 2011

A network model featuring a state space mixture prior that tracks complex actor latent role changes through time and its utility as a network analysis tool is demonstrated, by applying it to United States Congress voting data.

Latent Space Approaches to Social Network Analysis

- Computer Science
- 2002

This work develops a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space,” and proposes Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates.

Model‐based clustering for social networks

- Computer Science
- 2007

A new model is proposed, the latent position cluster model, under which the probability of a tie between two actors depends on the distance between them in an unobserved Euclidean ‘social space’, and the actors’ locations in the latent social space arise from a mixture of distributions, each corresponding to a cluster.

Inference in Curved Exponential Family Models for Networks

- Mathematics, Computer Science
- 2006

This article first reviews the method of maximum likelihood estimation using Markov chain Monte Carlo in the context of fitting linear ERGMs, then extends this methodology to the situation where the model comes from a curved exponential family.

A state-space mixed membership blockmodel for dynamic network tomography

- Computer Science
- 2010

A model-based approach to analyze what is referred to as the dynamic tomography of time-evolving networks, which offers an intuitive but powerful tool to infer the semantic underpinnings of each actor, such as its social roles or biological functions, underlying the observed network topologies.