# A Review of Latent Space Models for Social Networks

arXiv: https://arxiv.org/abs/2012.02307 · HTML: https://mlsanity.com/paper/2012.02307/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations](https://mlsanity.com/paper/2204.05885/) (2022)
- [Continuous Latent Position Models for Instantaneous Interactions](https://mlsanity.com/paper/2103.17146/) (2021)
- [A Latent Space Model for Multilayer Network Data](https://mlsanity.com/paper/2102.09560/) (2021)
- [SteinGen: Generating Fidelitous and Diverse Graph Samples](https://mlsanity.com/paper/2403.18578/) (2024)
- [An unified approach to link prediction in collaboration networks](https://mlsanity.com/paper/2411.01066/) (2024)

## Abstract (arXiv)

In this paper, we provide a review on both fundamentals of social networks and latent space modeling. The former discusses important topics related to network description, including vertex characteristics and network structure; whereas the latter articulates relevant advances in network modeling, including random graph models, generalized random graph models, exponential random graph models, and social space models. We discuss in detail several latent space models provided in literature, providing special attention to distance, class, and eigen models in the context of undirected, binary networks. In addition, we also examine empirically the behavior of these models in terms of prediction and goodness-of-fit using more than twenty popular datasets of the network literature.