Imputation of Missing Links and Attributes in Longitudinal Social Surveys

Ouzienko, Vladimir; Obradović, Zoran

doi:10.1109/icdmw.2011.97

Cited by 6 publications

(7 citation statements)

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“…Model-based methods for missing cross-sectional network data were proposed by Robins et al (2004), Handcock and Giles (2010), Koskinen et al (2010Koskinen et al ( , 2013, all within the family of exponential random graph models. Imputation methods for crosssectional network data were proposed and examined by Huisman (2009), Wang et al (2016), Huisman and Krause (2017), and Krause et al (2018b), and for missing longitudinal network data by Huisman and Steglich (2008), Ouzienko and Obradovic (2014), Hipp et al (2015), and Krause et al (2018a). A combination of available case strategies and imputation within SAOMs (i.e., the default method implemented in the SIENA software) was examined by Hipp et al (2015), de la Haye et al (2017), and Krause et al (2018a).…”

Section: Treatments For Missing Behavior Datamentioning

confidence: 99%

“…Imputation methods for behavior data are scarcely investigated. Ouzienko and Obradovic (2014) present an ERGM-based imputation model for imputing both missing tie variables and missing actor behaviors for longitudinal network data. For missing behavior variable in SAOMs, Ripley et al (2017) propose a simple imputation scheme in which either the previous observation, the next observation, or the mode of the variable is imputed, in order of availability.…”

Section: Treatments For Missing Behavior Datamentioning

confidence: 99%

“…Koskinen et al (2013) illustrate the effect of missing behavior data and present an ERGM-based procedure to analyze the incomplete data. Ouzienko and Obradovic (2014) propose an ERGM-based imputation procedure for the case of longitudinal network data (i.e., temporal ERGMs). In a small simulation study, using simulated and real-life data, they showed that, in general, their imputations result in more accuracy in predicting tie and behavior variables (comparing observed and imputed scores) than simpler methods.…”

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confidence: 99%

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Missing behavior data in longitudinal network studies: the impact of treatment methods on estimated effect parameters in stochastic actor oriented models

Zandberg

Huisman

2019

Soc. Netw. Anal. Min.

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Section: Treatments For Missing Behavior Datamentioning

confidence: 99%

Section: Treatments For Missing Behavior Datamentioning

confidence: 99%

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confidence: 99%

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Missing behavior data in longitudinal network studies: the impact of treatment methods on estimated effect parameters in stochastic actor oriented models

Zandberg

Huisman

2019

Soc. Netw. Anal. Min.

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“…One of the standard ways of handling missing values is imputing values based on some predictive model, and then applying the analysis on a fully observed dataset. To exploit the graph structure, previous studies have proposed imputation of missing values based on the exponential random graph model [15]. The limitation of such an approach is that it is slow, as it requires Gibbs sampling, and so it cannot handle large graphs.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised learning for structured regression on partially observed attributed graphs

Stojanović

Jovanović

Gligorijevic

et al. 2015

Proceedings of the 2015 SIAM International Conference on Data Mining

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Conditional probabilistic graphical models provide a powerful framework for structured regression in spatio-temporal datasets with complex correlation patterns. However, in real-life applications a large fraction of observations is often missing, which can severely limit the representational power of these models. In this paper we propose a Marginalized Gaussian Conditional Random Fields (m-GCRF) structured regression model for dealing with missing labels in partially observed temporal attributed graphs. This method is aimed at learning with both labeled and unlabeled parts and effectively predicting future values in a graph. The method is even capable of learning from nodes for which the response variable is never observed in history, which poses problems for many state-of-the-art models that can handle missing data. The proposed model is characterized for various missingness mechanisms on 500 synthetic graphs. The benefits of the new method are also demonstrated on a challenging application for predicting precipitation based on partial observations of climate variables in a temporal graph that spans the entire continental US. We also show that the method can be useful for optimizing the costs of data collection in climate applications via active reduction of the number of weather stations to consider. In experiments on these real-world and synthetic datasets we show that the proposed model is consistently more accurate than alternative semi-supervised structured models, as well as models that either use imputation to deal with missing values or simply ignore them altogether.

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“…Koskinen et al (2013) illustrate the effect of missing behavior data and present an ERGM-based procedure to analyze the incomplete data. Ouzienko and Obradovic (2014) propose an ERGM-based imputation procedure for the case of longitudinal network data (i.e., temporal ERGMs). In a small simulation study, using simulated and reallife data, they showed that, in general, their imputations result in more accuracy in predicting tie and behavior variables (comparing observed and imputed scores) than simpler methods.…”

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confidence: 99%

Middle manager’s innovative work behavior and their social network position: A search on slippery ice

Zandberg¹

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In this positive perspective, middle managers are considered crucial for an organization's performance and success. One of the fundamental raisons d'être for a middle manager is to implement strategies and other decisions taken by top level managers. Middle managers collect and filter information from inside and outside the organization, interpret it and 1.3Scope of the thesis Innovative Work BehaviorInnovative work behavior is related to innovation. OECD (2018, p. 20) defines business innovation as "a new or improved product or business process (or combination thereof) that differs significantly from the firm's previous products or business processes and that has been introduced on the market or brought into use by the firm."Innovative work behavior is to a certain extent a social process, in which communication and interaction with others support and lead to creativity (Perry-Smith, 2006). This is typically a role for middle managers, which differs from top management in several key aspects (Kauppila, Bizzi, & Obstfeld, 2018): top managers often consider only a limited number of options and respond quickly to external developments. Conversely, middle managers who bring people and their diverse ideas together, have more impact on an organization's Individual characteristics of the middle managerNext to their social network structure, personal characteristics may help to explain why some middle managers are more innovative than others. In this thesis I focus on personality and on motivation and goal orientation.Personality traits are relatively stable over longer periods of time and therefore suited to explain differences in innovative work behavior (see Anderson, Potocnik, & Zhou, 2014, for an overview of the research in this area). In particular, the relationship between traits and creativity received a lot of attention (Abdullah, Omar, & Panatik, 2016;Anderson, Potocnik, & Zhou, 2014;Perry-Smith & Mannucci, 2017). Research on the influence of personality traits on innovative work behavior has paid much attention to the relation between the personality traits of the Five Factor Model and innovative work behavior. Of these five factors, openness to experience and conscientiousness in particular have been found to be associated with innovative work behavior (Baer, 2010;Woods et al., 2018). Individuals scoring high on conscientiousness are more dependable and committed, therefore it is often assumed that people scoring high on conscientiousness are highly motivated to find new solutions to problems that arise, or to make use of opportunities that evolve (Judge et al., The autonomy of a middle managerNext to a middle manager's personal characteristics or social network position, the organizational context plays a role in explaining innovative work behavior. A middle manager is an actor in a larger organization and has only limited autonomy for innovative work behavior. It is generally believed that a lack of autonomy will constrain a middle manager's innovative work behavior. For example, theories on corporate ent...

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Imputation of Missing Links and Attributes in Longitudinal Social Surveys

Cited by 6 publications

References 45 publications

Missing behavior data in longitudinal network studies: the impact of treatment methods on estimated effect parameters in stochastic actor oriented models

Missing behavior data in longitudinal network studies: the impact of treatment methods on estimated effect parameters in stochastic actor oriented models

Semi-supervised learning for structured regression on partially observed attributed graphs

Middle manager’s innovative work behavior and their social network position: A search on slippery ice

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