Roger Leenders
Biographic Data
| ID | 204408 |
|---|---|
| NAME | Roger Leenders |
| GIVEN NAMES | Roger |
| FAMILY NAME | Leenders |
| SIGNATURE | LEENDERS R |
| AFFILIATIONS | Tilburg University |
| ORCID | 0000-0002-0556-2550 |
| VERIFIED | Yes |
| TOTAL WORKS | 21 |
| TOTAL CITATIONS | 57 |
| AUTHOR COUNT | 20 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 1995 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 4 |
Not all bonds are created equal: Dyadic latent class models for relational event data
Dynamic social networks can be conceptualized as sequences of dyadic interactions between individuals over time. The relational event model has been the workhorse to analyze such interaction sequences in empirical social network research. When addressing possible unobserved heterogeneity in the interaction mechanisms, standard approaches, such as the stochastic block model, aim to cluster the variation at the actor level. Though useful, the impli…
Simulating relational event history data: Why and How
Many important social phenomena are characterized by repeated interactions among individuals over time such as email exchanges in an organization or face-to-face interactions in a classroom. To understand the underlying mechanisms of social interaction dynamics, statistical simulation techniques for network data at fine temporal granularity are crucial. This article makes two contributions to the field. First, we present statistical frameworks to…
A state-space relational event modeling approach for learning dynamic social interaction behavior
Relational event models (REMs) are the primary choice for the analysis of relational-event network data. However, the standard REM assumes static parameters, which hinders the modeling of time-varying dynamics. This assumption might be too restrictive in real-life scenarios, making a model that allows for time-varying parameters more valuable. We introduce a state-space extension of the relational event model as a way to tackle this problem. The …
A Bayesian Semi-Parametric Approach for Modeling Memory Decay in Dynamic Social Networks
In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that oc…
Separating the wheat from the chaff: Bayesian regularization in dynamic social networks
In recent years there has been an increasing interest in the use of relational event models for dynamic social network analysis. The basis of these models is the concept of an “event”, defined as a triplet of time, sender, and receiver of some social interaction. The key question that relational event models aim to answer is what drives the pattern of social interactions among actors. Researchers often consider a very large number of predictors i…
What is the Point of Change? Change Point Detection in Relational Event Models
This paper presents an extension to the relational event model with change points (REM-CP) to study abrupt changes to social interaction behavior in temporal networks. A change point detection algorithm is proposed for exploring when and which network effects abruptly change, and a confirmatory approach to test the presence of a change point at a given moment. The effectiveness of the methodology was assessed with numerical simulations and NASA’s…
Rivalries, reputation, retaliation, and repetition: Testing plausible mechanisms for the contagion of violence between street gangs using relational event models
The hypothesis that violence—especially gang violence—behaves like a contagious disease has grown in popularity in recent years. Scholars have long observed the tendency for violence to cluster in time and space, but little research has focused on empirically unpacking the mechanisms that make violence contagious. In the context of gang violence, retaliation is the prototypical mechanism to explain why violence begets violence. In this study, we …
How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models
In relational event networks, endogenous statistics are used to summarize the past activity between actors. Typically, it is assumed that past events have equal weight on the social interaction rate in the (near) future regardless of the time that has transpired since observing them. Generally, it is unrealistic to assume that recently past events affect the current event rate to an equal degree as long-past events. Alternatively one may consider…
The impact of social network change due to spousal loss: A qualitative study on experiences of older adults who are aging in place
Spousal loss due to nursing home admission or death is challenging for the well-being of the remaining partner and for aging in place. We explored: "How does social network change due to spousal loss impact older adults who are aging in place?." In-depth interviews were held with six older women who were aging in place and who lost their spouses in the past two years. Narrative analysis was conducted. Results indicate that the impact varies in th…
Network Autocorrelation Modeling: Bayesian Techniques for Estimating and Testing Multiple Network Autocorrelations
The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social influence are present simultaneously and can be modeled using various connectivity matrices. Often, researchers have expectations about the order of strength of these different influence mechanisms. However, currently available methods cannot be applied to…
Exploring the impact of social network change: Experiences of older adults ageing in place
Social networks are sources of support and contribute to the well-being of older adults who are ageing in place. As social networks change, especially when accompanied by health decline, older adults' sources of support change and their well-being is challenged. Previous studies predominantly used quantitative measures to examine how older adults' social networks change. Alternatively, this study explores the impact of changing social networks on…
Network Autocorrelation Modeling: A Bayes Factor Approach for Testing (Multiple) Precise and Interval Hypotheses
Currently available (classical) testing procedures for the network autocorrelation can only be used for falsifying a precise null hypothesis of no network effect. Classical methods can be neither used for quantifying evidence for the null nor for testing multiple hypotheses simultaneously. This article presents flexible Bayes factor testing procedures that do not have these limitations. We propose Bayes factors based on an empirical and a uniform…
Entrepreneurial networking: A blessing or a curse? Differential effects for low, medium and high performing franchisees
Recent studies have called for a better understanding of the link between networking and entrepreneurial performance. We provide such understanding in three ways: by focusing on a specific entrepreneurial context (franchise systems), by developing a multi-faceted theoretical framework and by highlighting a contingency that may affect the networking-performance link. We combine knowledge and learning perspectives with a networking perspective to d…
Bayesian estimation of the network autocorrelation model
Social Capital
Corporate Social Capital and Liability
Creating Trust through Narrative Strategy
In the rational model of trust, an actor's level of trust is a function of the expected gain and loss involved. As a consequence, if A can influence the gain and loss perceived by B, A can influence B's level of trust. In this article we study how A can do this through the strategic use of narrative. In particular, we show how actors who attempt to recruit others into network marketing employ narrative in manipulating the recruit's expectations. …
Modeling social influence through network autocorrelation: Constructing the weight matrix
Social Capital of Organizations
An influential concept in the social science literature, the theory of social capital, asserts that social actors gain resources through relationships with other actors. While social capital research has largely focused on individuals as the unit of analysis, this volume looks at social capital of organizations, or Corporate Social Capital (CSC), exploring how and to what extent social networks facilitate or impede the attainment of organizationa…
Evolution of friendship and best friendship choices
It has been recognized in the literature that the mechanisms driving friendship choices differ when different settings are considered. At the same time, it is likely that different types of friendships are governed by different mechanisms. Employing longitudinal sociometric data from classrooms in elementary schools, it is examined whether gender similarity, reciprocity, and proximity (joint membership of study groups) have similar effects on 'fr…
Models for network dynamics: A Markovian framework
A question not very often addressed in social network analysis relates to network dynamics and focuses on how networks arise and change. It alludes to the idea that ties do not arise or vanish randomly, but (partly) as a consequence of human behavior and preferences. Statistical models for modeling changes in the structure of social networks are rare and often strongly restricted substantively. The common approach is to focus on conditional trans…
Models for network dynamics: A Markovian framework
A question not very often addressed in social network analysis relates to network dynamics and focuses on how networks arise and change. It alludes to the idea that ties do not arise or vanish randomly, but (partly) as a consequence of human behavior and preferences. Statistical models for modeling changes in the structure of social networks are rare and often strongly restricted substantively. The common approach is to focus on conditional trans…
Evolution of friendship and best friendship choices
It has been recognized in the literature that the mechanisms driving friendship choices differ when different settings are considered. At the same time, it is likely that different types of friendships are governed by different mechanisms. Employing longitudinal sociometric data from classrooms in elementary schools, it is examined whether gender similarity, reciprocity, and proximity (joint membership of study groups) have similar effects on 'fr…
Rivalries, reputation, retaliation, and repetition: Testing plausible mechanisms for the contagion of violence between street gangs using relational event models
The hypothesis that violence—especially gang violence—behaves like a contagious disease has grown in popularity in recent years. Scholars have long observed the tendency for violence to cluster in time and space, but little research has focused on empirically unpacking the mechanisms that make violence contagious. In the context of gang violence, retaliation is the prototypical mechanism to explain why violence begets violence. In this study, we …
How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models
In relational event networks, endogenous statistics are used to summarize the past activity between actors. Typically, it is assumed that past events have equal weight on the social interaction rate in the (near) future regardless of the time that has transpired since observing them. Generally, it is unrealistic to assume that recently past events affect the current event rate to an equal degree as long-past events. Alternatively one may consider…
A Bayesian Semi-Parametric Approach for Modeling Memory Decay in Dynamic Social Networks
In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that oc…
Network Autocorrelation Modeling: Bayesian Techniques for Estimating and Testing Multiple Network Autocorrelations
The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social influence are present simultaneously and can be modeled using various connectivity matrices. Often, researchers have expectations about the order of strength of these different influence mechanisms. However, currently available methods cannot be applied to…
Exploring the impact of social network change: Experiences of older adults ageing in place
Social networks are sources of support and contribute to the well-being of older adults who are ageing in place. As social networks change, especially when accompanied by health decline, older adults' sources of support change and their well-being is challenged. Previous studies predominantly used quantitative measures to examine how older adults' social networks change. Alternatively, this study explores the impact of changing social networks on…
Models for network dynamics: A Markovian framework
A question not very often addressed in social network analysis relates to network dynamics and focuses on how networks arise and change. It alludes to the idea that ties do not arise or vanish randomly, but (partly) as a consequence of human behavior and preferences. Statistical models for modeling changes in the structure of social networks are rare and often strongly restricted substantively. The common approach is to focus on conditional trans…
Evolution of friendship and best friendship choices
It has been recognized in the literature that the mechanisms driving friendship choices differ when different settings are considered. At the same time, it is likely that different types of friendships are governed by different mechanisms. Employing longitudinal sociometric data from classrooms in elementary schools, it is examined whether gender similarity, reciprocity, and proximity (joint membership of study groups) have similar effects on 'fr…
Social Capital of Organizations
An influential concept in the social science literature, the theory of social capital, asserts that social actors gain resources through relationships with other actors. While social capital research has largely focused on individuals as the unit of analysis, this volume looks at social capital of organizations, or Corporate Social Capital (CSC), exploring how and to what extent social networks facilitate or impede the attainment of organizationa…
Modeling social influence through network autocorrelation: Constructing the weight matrix
Creating Trust through Narrative Strategy
In the rational model of trust, an actor's level of trust is a function of the expected gain and loss involved. As a consequence, if A can influence the gain and loss perceived by B, A can influence B's level of trust. In this article we study how A can do this through the strategic use of narrative. In particular, we show how actors who attempt to recruit others into network marketing employ narrative in manipulating the recruit's expectations. …
Corporate Social Capital and Liability
Social Capital
Bayesian estimation of the network autocorrelation model
Entrepreneurial networking: A blessing or a curse? Differential effects for low, medium and high performing franchisees
Recent studies have called for a better understanding of the link between networking and entrepreneurial performance. We provide such understanding in three ways: by focusing on a specific entrepreneurial context (franchise systems), by developing a multi-faceted theoretical framework and by highlighting a contingency that may affect the networking-performance link. We combine knowledge and learning perspectives with a networking perspective to d…
Exploring the impact of social network change: Experiences of older adults ageing in place
Social networks are sources of support and contribute to the well-being of older adults who are ageing in place. As social networks change, especially when accompanied by health decline, older adults' sources of support change and their well-being is challenged. Previous studies predominantly used quantitative measures to examine how older adults' social networks change. Alternatively, this study explores the impact of changing social networks on…
Network Autocorrelation Modeling: A Bayes Factor Approach for Testing (Multiple) Precise and Interval Hypotheses
Currently available (classical) testing procedures for the network autocorrelation can only be used for falsifying a precise null hypothesis of no network effect. Classical methods can be neither used for quantifying evidence for the null nor for testing multiple hypotheses simultaneously. This article presents flexible Bayes factor testing procedures that do not have these limitations. We propose Bayes factors based on an empirical and a uniform…
Network Autocorrelation Modeling: Bayesian Techniques for Estimating and Testing Multiple Network Autocorrelations
The network autocorrelation model has been the workhorse for estimating and testing the strength of theories of social influence in a network. In many network studies, different types of social influence are present simultaneously and can be modeled using various connectivity matrices. Often, researchers have expectations about the order of strength of these different influence mechanisms. However, currently available methods cannot be applied to…
The impact of social network change due to spousal loss: A qualitative study on experiences of older adults who are aging in place
Spousal loss due to nursing home admission or death is challenging for the well-being of the remaining partner and for aging in place. We explored: "How does social network change due to spousal loss impact older adults who are aging in place?." In-depth interviews were held with six older women who were aging in place and who lost their spouses in the past two years. Narrative analysis was conducted. Results indicate that the impact varies in th…
Separating the wheat from the chaff: Bayesian regularization in dynamic social networks
In recent years there has been an increasing interest in the use of relational event models for dynamic social network analysis. The basis of these models is the concept of an “event”, defined as a triplet of time, sender, and receiver of some social interaction. The key question that relational event models aim to answer is what drives the pattern of social interactions among actors. Researchers often consider a very large number of predictors i…
What is the Point of Change? Change Point Detection in Relational Event Models
This paper presents an extension to the relational event model with change points (REM-CP) to study abrupt changes to social interaction behavior in temporal networks. A change point detection algorithm is proposed for exploring when and which network effects abruptly change, and a confirmatory approach to test the presence of a change point at a given moment. The effectiveness of the methodology was assessed with numerical simulations and NASA’s…
Rivalries, reputation, retaliation, and repetition: Testing plausible mechanisms for the contagion of violence between street gangs using relational event models
The hypothesis that violence—especially gang violence—behaves like a contagious disease has grown in popularity in recent years. Scholars have long observed the tendency for violence to cluster in time and space, but little research has focused on empirically unpacking the mechanisms that make violence contagious. In the context of gang violence, retaliation is the prototypical mechanism to explain why violence begets violence. In this study, we …
How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models
In relational event networks, endogenous statistics are used to summarize the past activity between actors. Typically, it is assumed that past events have equal weight on the social interaction rate in the (near) future regardless of the time that has transpired since observing them. Generally, it is unrealistic to assume that recently past events affect the current event rate to an equal degree as long-past events. Alternatively one may consider…
A state-space relational event modeling approach for learning dynamic social interaction behavior
Relational event models (REMs) are the primary choice for the analysis of relational-event network data. However, the standard REM assumes static parameters, which hinders the modeling of time-varying dynamics. This assumption might be too restrictive in real-life scenarios, making a model that allows for time-varying parameters more valuable. We introduce a state-space extension of the relational event model as a way to tackle this problem. The …
A Bayesian Semi-Parametric Approach for Modeling Memory Decay in Dynamic Social Networks
In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that oc…
Simulating relational event history data: Why and How
Many important social phenomena are characterized by repeated interactions among individuals over time such as email exchanges in an organization or face-to-face interactions in a classroom. To understand the underlying mechanisms of social interaction dynamics, statistical simulation techniques for network data at fine temporal granularity are crucial. This article makes two contributions to the field. First, we present statistical frameworks to…
Not all bonds are created equal: Dyadic latent class models for relational event data
Dynamic social networks can be conceptualized as sequences of dyadic interactions between individuals over time. The relational event model has been the workhorse to analyze such interaction sequences in empirical social network research. When addressing possible unobserved heterogeneity in the interaction mechanisms, standard approaches, such as the stochastic block model, aim to cluster the variation at the actor level. Though useful, the impli…
Computer Science (15 works) · Mathematics (11 works) · Psychology (10 works) · Statistics (9 works) · Complex Network Analysis Techniques (8 works) · Econometrics (8 works) · Artificial Intelligence (7 works) · Bayesian probability (6 works) · Opinion Dynamics and Social Influence (6 works) · Social Psychology (6 works)