Continuous Time Graph Processes with Known ERGM Equilibria: Contextual Review, Extensions, and Synthesis
arXiv:2203.06948 · doi:10.1080/0022250X.2023.2180001
Abstract
Graph processes that unfold in continuous time are of obvious theoretical and practical interest. Particularly useful are those whose long-term behavior converges to a graph distribution of known form. Here, we review some of the conditions for such convergence, and provide examples of novel and/or known processes that do so. These include subfamilies of the well-known stochastic actor oriented models, as well as continuum extensions of temporal and separable temporal exponential family random graph models. We also comment on some related threads in the broader work on network dynamics, which provide additional context for the continuous time case.
arXiv commentFinal accepted version
References in corpus (14)
- Proceedings of the 29th International Conference on Machine Learning (ICML-12)
- A Separable Model for Dynamic Networks
- Modeling social networks from sampled data
- Exponential-Family Random Graph Models for Valued Networks
- Maximum likelihood estimation for social network dynamics
- Solution of the 2-star model of a network
- Point process modeling for directed interaction networks
- Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models
- Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios
- On the Question of Effective Sample Size in Network Modeling: An Asymptotic Inquiry
- Phase transitions in exponential random graphs
- A Dynamic Process Interpretation of the Sparse ERGM Reference Model
- A Perfect Sampling Method for Exponential Family Random Graph Models
- Modeling of Dynamic Networks based on Egocentric Data with Durational Information