Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles

Block, Per; Stadtfeld, Christoph; Snijders, Tom A. B.

doi:10.1177/0049124116672680

Cited by 84 publications

(84 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A significant part of this article was concerned with contrasting the DyNAM to the tie-oriented REM. We explained the theoretically different assumptions of actorand tie-oriented network models, building upon the cross-sectional comparison of Block et al (2016). The DyNAM consists of two sub processes.…”

Section: Discussionmentioning

confidence: 99%

“…The differences are that the REM contains one modeling step (which event will happen next), whereas the DyNAM contains two steps (who will send an event, and to whom; see Figure 3). 7 As a consequence, first, as opposed to the REM all modeling decisions are nested within actors (Block et al 2016). This means that the probability to observe events in the REM only depends on its embedding in structures described by included effects.…”

Section: Revisiting the Actor-oriented Paradigmmentioning

confidence: 99%

See 1 more Smart Citation

Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events

Stadtfeld¹,

Block²

2017

SocScience

Self Cite

View full text Add to dashboard Cite

Ample theoretical work on social networks is explicitly or implicitly concerned with the role of interpersonal interaction. However, empirical studies to date mostly focus on the analysis of stable relations. This article introduces Dynamic Network Actor Models (DyNAMs) for the study of directed, interpersonal interaction through time. The presented model addresses three important aspects of interpersonal interaction. First, interactions unfold in a larger social context and depend on complex structures in social systems. Second, interactions emanate from individuals and are based on personal preferences, restricted by the available interaction opportunities. Third, sequences of interactions develop dynamically, and the timing of interactions relative to one another contains useful information. We refer to these aspects as the network nature, the actor-oriented nature, and the dynamic nature of social interaction. A case study compares the DyNAM framework to the relational event model, a widely used statistical method for the study of social interaction data.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Revisiting the Actor-oriented Paradigmmentioning

confidence: 99%

Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events

Stadtfeld¹,

Block²

2017

SocScience

Self Cite

View full text Add to dashboard Cite

show abstract

“… SI Appendix , section 2.2 provides detailed results and robustness checks using exponential random graph models for cross-sectional network data (25). [The two models differ in parameter interpretation (26), but both suggest a strong tie-level association between the networks. ]…”

Section: The Emergence Of Networkmentioning

confidence: 99%

Integration in emerging social networks explains academic failure and success

Stadtfeld

Vörös

Elmer

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

157

131

View full text Add to dashboard Cite

SignificanceUnderstanding the factors that explain academic failure and success of university students is a core interest of educational researchers, teachers, and managers. We demonstrate how the dynamic social networks that informally evolve between students can affect their academic performance. We closely followed the emergence of multiple social networks within a cohort of 226 undergraduate university students. They were strangers to each other on their first day at university, but developed densely knit social networks through time. We show that functional studying relationships tended to evolve from informal friendship relations. In a critical examination period after one year, these networks proved to be crucial: Socially isolated students had significantly lower examination grades and were more likely to drop out of university.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles

Cited by 84 publications

References 35 publications

Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events

Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events

Integration in emerging social networks explains academic failure and success

Automatic Discovery of Families of Network Generative Processes

Contact Info

Product

Resources

About