Change we can believe in: Comparing longitudinal network models on consistency, interpretability and predictive power

Block, Per; Koskinen, Johan; Hollway, James; Steglich, Christian; Stadtfeld, Christoph

doi:10.1016/j.socnet.2017.08.001

Cited by 81 publications

(80 citation statements)

References 44 publications

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“…Naturally these models are not without their methodological critics (Block, Koskinen, Hollway, Steglich, & Stadtfeld, 2018). And, like any other statistical methodologies utilized, a model cannot correct for bad measurement.…”

Section: Methodological Advances: Increasingly Flexible Network Modelsmentioning

confidence: 99%

Reconceptualizing the Policy Subsystem: Integration with Complexity Theory and Social Network Analysis

McGee¹,

Jones²

2019

Policy Studies Journal

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The concept of the policy subsystem is an essential building block for several of the basic frameworks of policy process studies. Over time issues have become more complex, crossing subsystem boundaries, and so subsystems have escalated in their complexity as well. It is increasingly insufficient to study just one policy subsystem and so scholars have turned to studying boundary‐spanning regimes or policy networks. In this essay, we review the major contributions to developing the concept of a policy subsystem and trace its evolution into broader conceptualizations like issue and policy networks. We argue that the future for theories of the policy process is in more explicit integration of complexity theory and more effective modeling of subsystems with the utilization of social network analysis. In closing, we discuss the enduring nature of the concept of policy subsystems and highlight studies that continue using it in innovative ways.

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Section: Methodological Advances: Increasingly Flexible Network Modelsmentioning

confidence: 99%

Reconceptualizing the Policy Subsystem: Integration with Complexity Theory and Social Network Analysis

McGee¹,

Jones²

2019

Policy Studies Journal

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“…Only in this setting the parameters allow for a meaningful interpretation. See Block et al (2018) for a deeper discussion. Hanneke et al (2010) is the main reference for the TERGM, a model class that utilizes the Markov structure and, thereby, assumes that the transition of a network from time Isolated countries are not depicted for clarity and the node size relates to its total degree.…”

Section: Temporal Exponential Random Graph Modelmentioning

confidence: 99%

“…In order to ensure comparable estimates we estimate the TERGM as well as the STERGM with the statnet library, using MCMC based likelihood inference techniques. We use the package ergm and include the lagged previous network as a dyadic covariate, which is in fact equivalent to the stability term (4) after some reformulation (see Block et al, 2018). The STERGM is fitted using the tergm package.…”

Section: Software and Applicationmentioning

confidence: 99%

“…Snijders (2005) discusses continuous time models and reframes the independence and reciprocity model as a Stochastic Actor oriented Model (SAOM, Snijders, 1996). Block et al (2018) provide an in-depth comparison of the Temporal Exponential Random Graph Model (TERGM, Hanneke et al, 2010) and the SAOM with special focus on the treatment of time. Further, the ERGM and SAOM for networks which are observed at single time points are contrasted by Block et al (2019), deriving theoretical guidelines for model selection based on the differing mechanics implied by each model.…”

Section: Introductionmentioning

confidence: 99%

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Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time

Fritz

Lebacher

Kauermann

2019

Statistica Neerlandica

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Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. It is often difficult to compare the different models with respect to their applicability and interpretation. The goal of this survey is to provide an overview of popular dynamic network models. The survey is focused on introducing binary network models with their corresponding assumptions, advantages, and shortfalls. The models are divided according to generating processes, operating in discrete and continuous time, respectively. First, we introduce the Temporal Exponential Random Graph Model (TERGM) and its extension, the Separable TERGM (STERGM), both being time-discrete models. These models are then contrasted with continuous process models, focusing on the Relational Event Model (REM). We additionally show how the REM can handle time-clustered observations, i.e., continuous time data observed at discrete time points. Besides the discussion of theoretical properties and fitting procedures, we specifically focus on the application of the models using a network that represents international arms transfers. The data allow to demonstrate the applicability and interpretation of the network models.

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“…ERGMs and SAOMs assuredly share several traits, and it is also possible to develop ERGMs in a longitudinal framework as temporal ERGMs (or TERGMs), where the estimation for a graph at time t depends on the graph at t − 1 [Hanneke et al, 2010]. For a more detailed comparison between SAOMs and ERGMs, see Block et al [2016Block et al [ , 2018.…”

Section: Using Macro-level Structurementioning

confidence: 99%

Automatic Discovery of Families of Network Generative Processes

Menezes¹,

Roth²

2019

Dynamics on and of Complex Networks III

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Designing plausible network models typically requires scholars to form a priori intuitions on the key drivers of network formation. Oftentimes, these intuitions are supported by the statistical estimation of a selection of network evolution processes which will form the basis of the model to be developed. Machine learning techniques have lately been introduced to assist the automatic discovery of generative models. These approaches may more broadly be described as "symbolic regression", where fundamental network dynamic functions, rather than just parameters, are evolved through genetic programming. This chapter first aims at reviewing the principles, efforts and the emerging literature in this direction, which is very much aligned with the idea of creating artificial scientists. Our contribution then aims more specifically at building upon an approach recently developed by us [Menezes & Roth, 2014] in order to demonstrate the existence of families of networks that may be described by similar generative processes. In other words, symbolic regression may be used to group networks according to their inferred genotype (in terms of generative processes) rather than their observed phenotype (in terms of statistical/topological features). Our empirical case is based on an original data set of 238 anonymized ego-centered networks of Facebook friends, further yielding insights on the formation of sociability networks.

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Change we can believe in: Comparing longitudinal network models on consistency, interpretability and predictive power

Cited by 81 publications

References 44 publications

Reconceptualizing the Policy Subsystem: Integration with Complexity Theory and Social Network Analysis

Reconceptualizing the Policy Subsystem: Integration with Complexity Theory and Social Network Analysis

Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time

Automatic Discovery of Families of Network Generative Processes

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