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In Chapter 6 and 7, Koskinen and Daraganova provide a more detailed methodological description of ERGMs based on a number of possible dependence assumptions and how these assumptions can be formalized further. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Although ERGMs represent the latest advancement in social network methodology, the class of models are in need of further development. [Extracted from the article]

In Chapter 9 the authors discuss the interpretation of the latent positions in the LSM: They discuss transformations which leave the model invariant and mention some remedies to these identification issues. Overall, I can recommend the book to anyone who has an interest in applying ERGMs or LSMs to better understand network data. In consideration of this, theorists and practitioners are facing new challenges when studying network models or working with network data. [Extracted from the article]

Hofstad, R Random Graphs and Complex Networks. vol. 1. New York: Cambridge University Press; 2017. 321 pages; ISBN: 978-1-107-17287-6 [ABSTRACT FROM AUTHOR]

The article reviews the book "The Strange Logic of Random Graphs," by J. Spencer.

Reviews the book "Random Graphs for Statistical Pattern Recognition," by D. Marchette.

The article reviews the book "Random Graph Dynamics," by Rick Durrett.

The article reviews the book "Random Graph Dynamics," by Rick Durrett.

The article reviews the book "Random Graphs for Statistical Pattern Recognition," by David J. Marchette.

The article reviews the book "Random Graph Dynamics," by Rick Durrett.

Reviews the book "Random Graphs for Statistical Pattern Recognition," by David J. Marchette.

The article reviews the book "Random Graphs," by Svante Janson, Tomasz Luczak and Andrzej Rucinski.