# A Two-Stage Working Model Strategy for Network Analysis Under Hierarchical Exponential Random Graph Models

@article{Cao2018ATW, title={A Two-Stage Working Model Strategy for Network Analysis Under Hierarchical Exponential Random Graph Models}, author={Ming Cao and Yong Chen and Kayo Fujimoto and Michael Schweinberger}, journal={2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)}, year={2018}, pages={290-298} }

Social network data are complex and dependent data. At the macro-level, social networks often exhibit clustering in the sense that social networks consist of communities; and at the micro-level, social networks often exhibit complex network features such as transitivity within communities. Modeling real-world social networks requires modeling both the macro- and micro-level, but many existing models focus on one of them while neglecting the other. In recent work, [28] introduced a class of…

## 2 Citations

Dynamic Network Data Protection Algorithm Using Differential Privacy in Internet of Things

- Computer Science2019 IEEE International Conference on Smart Internet of Things (SmartIoT)
- 2019

Theoretical analysis and experiments show that the algorithm proposed in this paper can preserve the important network structure characteristics of the original graph under the premise of satisfying the differential privacy protection model.

An analysis of connectivity, assortativity and cluster structure of the Asian-Australasian cruise shipping network

- Maritime Transport Research
- 2022

## References

SHOWING 1-10 OF 33 REFERENCES

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.

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.

Goodness of Fit of Social Network Models

- Computer Science
- 2008

A systematic examination of a real network data set using maximum likelihood estimation for exponential random graph models as well as new procedures to evaluate how well the models fit the observed networks concludes that these models capture aspects of the social structure of adolescent friendship relations not represented by previous models.

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.

Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models

- Computer ScienceSoc. Networks
- 2009

Pseudolikelihood Estimation for Social Networks

- Computer Science
- 1990

The focus of this article is on a general estimation technique that maximizes the pseudolikelihood, the maximum likelihood of log-linear modeling for social-network data.

Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp

- Computer Science
- 1996

A large class of models, including several generalizations of stochastic block models, as well as models parameterizing global tendencies towards clustering and centralization, and individual differences in such tendencies are described and extended.

Assessing Degeneracy in Statistical Models of Social Networks

- Computer Science
- 2003

This paper presents recent advances in the statistical modeling of random graphs that have an impact on the empirical study of social networks and issues of the model degeneracy and inferential degeneracy for commonly used estimators.

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.

Fitting Position Latent Cluster Models for Social Networks with latentnet.

- Computer ScienceJournal of statistical software
- 2008

Latentnet is a package to fit and evaluate statistical latent position and cluster models for networks and provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering.