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A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model – Corrigendum
... We also include a memory term, which indicates whether donors have given adaptation aid to the same recipients in the previous year. The term increases in size the less the network changes over the years (Leifeld and Cranmer, 2015). The memory term thus examines whether network ties from previous periods influence network formation in later years. ...
This article examines how adaptation aid is allocated across countries, and specifically focus on the role of donor—donor interactions in allocation decisions. We test two contrasting hypotheses: the presence of other adaptation donors in a recipient country may increase or reduce the likelihood of donor i to provide adaptation aid to that recipient. In the former case, donors support adaptation in the same recipient countries; in the latter, they provide their adaptation aid to different recipient countries. We model adaptation aid allocations as a network, and apply an innovative method, bipartite temporal exponential random graph models, to bilateral adaptation aid flows between 2010 and 2016. Our empirical analysis finds strong evidence for donor interactions. The results suggest a positive effect of other donors: donors tend to support adaptation in similar sets of recipient countries. These results provide further evidence that adaptation aid largely follow the structures and processes of traditional development aid, which poses questions for the additionality of finance for adaptation to climate change.
... The models' overall convergence is measured by a maximum convergence ratio, formulated through the same logic (for details, see Ripley et al., 2018). We assess models' goodness of fit by using the estimated parameters to simulate the transition of networks from a preceding period, then compare unmodeled endogenous features of the simulated networks to the same features in the observed network (Leifeld & Cranmer, 2016;Lospinoso, 2012). Table 5 presents the results of the three models. ...
How do sociocultural dynamics shape conflict? We develop a relational understanding of how social relations, culture, and conflict are interwoven. Using this framework, we examine how combatants’ associations with cultural elements affect the interpersonal relationships underlying conflict dynamics, as well as how these relationships engender associations to cultural elements. To do so, we first introduce a novel analytical approach that synthesizes computational textual analysis and stochastic actor-oriented models of longitudinal networks. We then use our approach to analyze a two-level socio-semantic graph representing both the cultural domain and social relationships of prominent militants operating in one Afghan province, Balkh, between 1979 and 2001. Our results indicate that militants’ interpersonal comradeships rely, in part, on their connections to cultural elements and relative power. Comradeship, in turn, fosters militants’ connections to cultural elements. We conclude by discussing how conflict studies can continue to build on insights from cultural sociology, as well as how cultural sociology and socio-semantic network research can benefit from further engaging conflict studies and developing our analytical approach. We also highlight provisional insights into endogenous mechanisms of conflict resolution and cultural change.
... Building on this work, Czarna, Leifeld, Śmieja, Dufner, and Salovey (2016) conducted a longitudinal study of 15 groups of Polish students, assessing grandiose narcissism, emotional intelligence, self-esteem, and network structure at zero acquaintance and again 3 months later. The authors used a specialized longitudinal social network analysis called a temporal exponential random graph model (TERGM; e.g., Leifeld & Cranmer, 2016). TERGM compares multiple timepoints of the same network and models the probability of ties between any two nodes appearing or disappearing over time as a function of structural and individual covariates. ...
Narcissism is an inherently interpersonal trait, which both affects and is affected by those around the narcissistic individual. This chapter describes the use of social network analysis (SNA) to study narcissism. A brief primer for SNA is presented, explaining how data are collected and analyzed from both online and face-to-face social networks. I subsequently review studies of narcissism in sociocentric, egocentric, and online social networks. The reviewed studies indicate that narcissists report a greater number of social connections but that these perceived connections are often not reciprocated by the other members of their networks. Evidence from SNA also demonstrates that narcissists are more disliked and experience greater conflict with members of their networks. Social networks provide substantial information about the interpersonal effects of narcissism which might be obscured by relying only on self-report. Future research applying a social network methodology to the study of narcissism is suggested. © Springer International Publishing AG, part of Springer Nature 2018.
We welcome our new editors and provide background on an unusual duo of articles in this issue.
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.
The temporal exponential random graph model (TERGM) and the stochastic
actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal
network analysis. We compare these models theoretically, via simulation, and
through a real-data example in order to assess their relative strengths and
weaknesses. Though one cannot make a general claim about either being superior
to the other across specifications, we find that the more restrictive
assumptions of the SAOM must be met exactly in order for it to perform
comparably to the TERGM. Otherwise, we find that the TERGM outperforms the SAOM
in out-of-sample prediction by substantial margins.
Replication Data for: A Theoretical and Empirical Comparison of the Temporal Exponential Random Graph Model and the Stochastic Actor-Oriented Model
- P Leifeld
- S Cranmer
Leifeld, P., & Cranmer, S. (2019b). Replication Data for: A Theoretical and Empirical Comparison of the Temporal
Exponential Random Graph Model and the Stochastic Actor-Oriented Model, https://doi.org/10.7910/DVN/NEM2XU,
Harvard Dataverse, V1.