Template-type: ReDIF-Article 1.0 Author-Name: Block, Per Author-Name: Hollway, James Author-Name: Stadtfeld, Christoph Author-Name: Koskinen, Johan Author-Name: Snijders, Tom Title: Circular specifications and “predicting” with information from the future: Errors in the empirical SAOM–TERGM comparison of Leifeld & Cranmer Journal: Network Science Pages: 3-14 Issue: 1 Volume: 10 Year: 2022 Month: March Abstract: We review the empirical comparison of Stochastic Actor-oriented Models (SAOMs) and Temporal Exponential Random Graph Models (TERGMs) by Leifeld & Cranmer in this journal [Network Science 7(1):20–51, 2019]. When specifying their TERGM, they use exogenous nodal attributes calculated from the outcome networks’ observed degrees instead of endogenous ERGM equivalents of structural effects as used in the SAOM. This turns the modeled endogeneity into circularity and obtained results are tautological. In consequence, their out-of-sample predictions using TERGMs are based on out-of-sample information and thereby predict the future using observations from the future. Thus, their analysis rests on erroneous model specifications that invalidate the article’s conclusions. Finally, beyond these specific points, we argue that their evaluation metric—tie-level predictive accuracy—is unsuited for the task of comparing model performance. File-URL: https://www.cambridge.org/core/product/identifier/S2050124222000066/type/journal_article File-Function: link to article abstract page File-Format: text/html Handle: RePEc:cup:netsci:v:10:y:2022:i:1:p:3-14_1 Template-type: ReDIF-Article 1.0 Author-Name: Leifeld, Philip Author-Name: Cranmer, Skyler J. Title: The stochastic actor-oriented model is a theory as much as it is a method and must be subject to theory tests Journal: Network Science Pages: 15-19 Issue: 1 Volume: 10 Year: 2022 Month: March Abstract: File-URL: https://www.cambridge.org/core/product/identifier/S2050124222000078/type/journal_article File-Function: link to article abstract page File-Format: text/html Handle: RePEc:cup:netsci:v:10:y:2022:i:1:p:15-19_2 Template-type: ReDIF-Article 1.0 Author-Name: Spencer, Neil A. Author-Name: Junker, Brian W. Author-Name: Sweet, Tracy M. Title: Faster MCMC for Gaussian latent position network models Journal: Network Science Pages: 20-45 Issue: 1 Volume: 10 Year: 2022 Month: March Abstract: Latent position network models are a versatile tool in network science; applications include clustering entities, controlling for causal confounders, and defining priors over unobserved graphs. Estimating each node’s latent position is typically framed as a Bayesian inference problem, with Metropolis within Gibbs being the most popular tool for approximating the posterior distribution. However, it is well-known that Metropolis within Gibbs is inefficient for large networks; the acceptance ratios are expensive to compute, and the resultant posterior draws are highly correlated. In this article, we propose an alternative Markov chain Monte Carlo strategy—defined using a combination of split Hamiltonian Monte Carlo and Firefly Monte Carlo—that leverages the posterior distribution’s functional form for more efficient posterior computation. We demonstrate that these strategies outperform Metropolis within Gibbs and other algorithms on synthetic networks, as well as on real information-sharing networks of teachers and staff in a school district. File-URL: https://www.cambridge.org/core/product/identifier/S2050124222000017/type/journal_article File-Function: link to article abstract page File-Format: text/html Handle: RePEc:cup:netsci:v:10:y:2022:i:1:p:20-45_3 Template-type: ReDIF-Article 1.0 Author-Name: Carlen, Jane Author-Name: de Dios Pont, Jaume Author-Name: Mentus, Cassidy Author-Name: Chang, Shyr-Shea Author-Name: Wang, Stephanie Author-Name: Porter, Mason A. Title: Role detection in bicycle-sharing networks using multilayer stochastic block models Journal: Network Science Pages: 46-81 Issue: 1 Volume: 10 Year: 2022 Month: March Abstract: In urban systems, there is an interdependency between neighborhood roles and transportation patterns between neighborhoods. In this paper, we classify docking stations in bicycle-sharing networks to gain insight into the human mobility patterns of three major cities in the United States. We propose novel time-dependent stochastic block models, with degree-heterogeneous blocks and either mixed or discrete block membership, which classify nodes based on their time-dependent activity patterns. We apply these models to (1) detect the roles of bicycle-sharing stations and (2) describe the traffic within and between blocks of stations over the course of a day. Our models successfully uncover work blocks, home blocks, and other blocks; they also reveal activity patterns that are specific to each city. Our work gives insights for the design and maintenance of bicycle-sharing systems, and it contributes new methodology for community detection in temporal and multilayer networks with heterogeneous degrees. File-URL: https://www.cambridge.org/core/product/identifier/S2050124221000217/type/journal_article File-Function: link to article abstract page File-Format: text/html Handle: RePEc:cup:netsci:v:10:y:2022:i:1:p:46-81_4 Template-type: ReDIF-Article 1.0 Author-Name: Karwa, Vishesh Author-Name: Petrović, Sonja Author-Name: Bajić, Denis Title: DERGMs: Degeneracy-restricted exponential family random graph models Journal: Network Science Pages: 82-110 Issue: 1 Volume: 10 Year: 2022 Month: March Abstract: Exponential random graph models, or ERGMs, are a flexible and general class of models for modeling dependent data. While the early literature has shown them to be powerful in capturing many network features of interest, recent work highlights difficulties related to the models’ ill behavior, such as most of the probability mass being concentrated on a very small subset of the parameter space. This behavior limits both the applicability of an ERGM as a model for real data and inference and parameter estimation via the usual Markov chain Monte Carlo algorithms. To address this problem, we propose a new exponential family of models for random graphs that build on the standard ERGM framework. Specifically, we solve the problem of computational intractability and “degenerate” model behavior by an interpretable support restriction. We introduce a new parameter based on the graph-theoretic notion of degeneracy, a measure of sparsity whose value is commonly low in real-world networks. The new model family is supported on the sample space of graphs with bounded degeneracy and is called degeneracy-restricted ERGMs, or DERGMs for short. Since DERGMs generalize ERGMs—the latter is obtained from the former by setting the degeneracy parameter to be maximal—they inherit good theoretical properties, while at the same time place their mass more uniformly over realistic graphs. The support restriction allows the use of new (and fast) Monte Carlo methods for inference, thus making the models scalable and computationally tractable. We study various theoretical properties of DERGMs and illustrate how the support restriction improves the model behavior. We also present a fast Monte Carlo algorithm for parameter estimation that avoids many issues faced by Markov Chain Monte Carlo algorithms used for inference in ERGMs. File-URL: https://www.cambridge.org/core/product/identifier/S2050124222000054/type/journal_article File-Function: link to article abstract page File-Format: text/html Handle: RePEc:cup:netsci:v:10:y:2022:i:1:p:82-110_5 Template-type: ReDIF-Article 1.0 Author-Name: Leifeld, Philip Author-Name: Cranmer, Skyler J. Title: A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model – Corrigendum Journal: Network Science Pages: 111-111 Issue: 1 Volume: 10 Year: 2022 Month: March Abstract: File-URL: https://www.cambridge.org/core/product/identifier/S205012422200011X/type/journal_article File-Function: link to article abstract page File-Format: text/html Handle: RePEc:cup:netsci:v:10:y:2022:i:1:p:111-111_6