More Like this

1. Decomposing Network Influence: Social Influence Regression

   https://doi.org/10.1017/pan.2025.10013  

   Minhas, Shahryar; Hoff, Peter D (August 2025, Political Analysis)

   Abstract Understanding network influence and its determinants are key challenges in political science and network analysis. Traditional latent variable models position actors within a social space based on network dependencies but often do not elucidate the underlying factors driving these interactions. To overcome this limitation, we propose the social influence regression (SIR) model, an extension of vector autoregression tailored for relational data that incorporates exogenous covariates into the estimation of influence patterns. The SIR model captures influence dynamics via a pair of$$n \times n$$matrices that quantify how the actions of one actor affect the future actions of another. This framework not only provides a statistical mechanism for explaining actor influence based on observable traits but also improves computational efficiency through an iterative block coordinate descent method. We showcase the SIR model’s capabilities by applying it to monthly conflict events between countries, using data from the Integrated Crisis Early Warning System (ICEWS). Our findings demonstrate the SIR model’s ability to elucidate complex influence patterns within networks by linking them to specific covariates. This paper’s main contributions are: (1) introducing a model that explains third-order dependencies through exogenous covariates and (2) offering an efficient estimation approach that scales effectively with large, complex networks. 

   more » « less
2. Bayesian analysis of social influence

   https://doi.org/10.1111/rssa.12844  

   Koskinen, Johan; Daraganova, Galina (June 2022, Journal of the Royal Statistical Society: Series A (Statistics in Society))
3. An Influence Propagation View of PageRank

   https://doi.org/10.1145/3046941  

   Liu, Qi; Xiang, Biao; Yuan, Nicholas Jing; Chen, Enhong; Xiong, Hui; Zheng, Yi; Yang, Yu (April 2017, ACM Transactions on Knowledge Discovery from Data)
4. The influence function of semiparametric estimators

   https://doi.org/10.3982/QE826  

   Ichimura, Hidehiko; Newey, Whitney K. (January 2022, Quantitative Economics)

   There are many economic parameters that depend on nonparametric first steps. Examples include games, dynamic discrete choice, average exact consumer surplus, and treatment effects. Often estimators of these parameters are asymptotically equivalent to a sample average of an object referred to as the influence function. The influence function is useful in local policy analysis, in evaluating local sensitivity of estimators, and constructing debiased machine learning estimators. We show that the influence function is a Gateaux derivative with respect to a smooth deviation evaluated at a point mass. This result generalizes the classic Von Mises (1947) and Hampel (1974) calculation to estimators that depend on smooth nonparametric first steps. We give explicit influence functions for first steps that satisfy exogenous or endogenous orthogonality conditions. We use these results to generalize the omitted variable bias formula for regression to policy analysis for and sensitivity to structural changes. We apply this analysis and find no sensitivity to endogeneity of average equivalent variation estimates in a gasoline demand application. 

   more » « less
5. Influence of heavy resonances in SMASH

   https://doi.org/10.31349/SuplRevMexFis.3.040921  

   Salinas San Martín, J.; Noronha-Hostler, J.; Elfner, H.; Hammelmann, J.; Hirayama, R. (December 2022, Suplemento de la Revista Mexicana de Física)

   Recent lattice QCD results, comparing to a hadron resonance gas model, have shown the need for hundreds of particles in hadronic models. These extra particles influence both the equation of state and hadronic interactions within hadron transport models. Here, we introduce the PDG21+ particle list, which contains the most up-to-date database of particles and their properties. We then convert all particles decays into 2 body decays so that they are compatible with SMASH in order to produce a more consistent description of a heavy-ion collision. 

   more » « less