scholarly journals A Statistical Model for the Analysis of Mobility Tables as Weighted Networks with an Application to Faculty Hiring Networks

2021 ◽  
Author(s):  
Per Block ◽  
Christoph Stadtfeld ◽  
Garry Robins
Keyword(s):  
Statistical Model ◽  
Linear Models ◽  
Statistical Modelling ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Faculty Hiring ◽  
Weighted Networks ◽  
Exponential Random Graph ◽  
Log Linear ◽  
Mobility Tables

Mobility of individuals between a wide variety of geographic locations, social positions, or roles is frequently analysed in the social sciences. In recent research, mobility has increasingly been conceptualised as a network. For example, residential mobility, when individuals move between neighbourhoods of a city, can be understood as a network in which neighbourhoods are nodes that are tied by counts of mobile individuals that move from one neighbourhood to another. Understanding mobility as a network allows to apply concepts and methods from the network analyst's toolbox. However, the statistical modelling of such weighted networks in which ties can have individual attributes remains difficult. In this article we propose a statistical model for the analysis of mobility tables conceptualised as networks, combining properties from log-linear models and exponential random graph models (ERGMs). When no endogenous patterns are modelled, it reduces to a classic log-linear model for mobility tables. When modelling endogenous patterns but ignoring individual attributes, the model can be understood as an ERGM for weighted networks in which tie weights denote counts. Making use of special constraints of mobility networks, the model offers a parsimonious way to deal with weighted ties. Going beyond classical ERG modelling, the proposed approach can additionally incorporate tie characteristics that represent individual attributes of mobile people. The model is applied to two cases of faculty hiring networks---linking current departments of faculty members with their PhD granting institution---in history and computer science in the US.

scholarly journals Exponential random graph models for little networks

2021 ◽  
Vol 64 ◽  
pp. 225-238
Author(s):  
George G. Vega Yon ◽  
Andrew Slaughter ◽  
Kayla de la Haye
Keyword(s):  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

scholarly journals Investigation of Rater Effects Using Social Network Analysis and Exponential Random Graph Models

2017 ◽  
Vol 78 (3) ◽  
pp. 430-459 ◽  
Author(s):  
Iasonas Lamprianou
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
High Stakes ◽  
Rating Data ◽  
New Approach ◽  
Exponential Random Graph ◽  
Rater Severity

It is common practice for assessment programs to organize qualifying sessions during which the raters (often known as “markers” or “judges”) demonstrate their consistency before operational rating commences. Because of the high-stakes nature of many rating activities, the research community tends to continuously explore new methods to analyze rating data. We used simulated and empirical data from two high-stakes language assessments, to propose a new approach, based on social network analysis and exponential graph models, to evaluate the readiness of a group of raters for operational rating. The results of this innovative approach are compared with the results of a Rasch analysis, which is a well-established approach for the analysis of such data. We also demonstrate how the new approach can be practically used to investigate important research questions such as whether rater severity is stable across rating tasks. The merits of the new approach, and the consequences for practice are discussed.

Why Legislative Networks? Analyzing Legislative Network Formation

2017 ◽  
Vol 7 (3) ◽  
pp. 505-522 ◽  
Author(s):  
Stefan Wojcik
Keyword(s):  
Political Parties ◽  
Network Formation ◽  
Contemporary Literature ◽  
Party Organization ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Personal Traits ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
The Social

Are the social networks of legislators affected more by their political parties or their personal traits? How does the party organization influence the tendency of members to work collectively on a day-to-day basis? In this paper, I explore the determinants of the relationships of legislators in the Brazilian Chamber of Deputies. I use exponential random graph models to evaluate the relative influence of personal traits versus party influence in generating legislator relationships. Despite a focus on personalism in Brazil, the analysis reveals that the effects of political parties on tie formation are roughly equal to the effects of personal traits, suggesting that networks may make political parties much more cohesive than contemporary literature would lead us to believe.

Following the Leader: The Evolving Network of Charter School Giving

2019 ◽  
Vol 121 (10) ◽  
pp. 1-32
Author(s):  
David Diehl ◽  
Robert A. Marx
Keyword(s):  
Charter Schools ◽  
Charter School ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
Disproportionate Number ◽  
Charter Management Organizations ◽  
Foundation Type ◽  
Over Time

Background/Context Research on the patterns of philanthropic funding of charter schools has largely focused on the behavior of major foundations. This work has documented how the once diffuse giving by these major foundations has become increasingly concentrated on a small number of jurisdictional challengers in the form of charter schools, charter management organizations, and intermediary organizations. Purpose The current study examines whether this convergence in giving has spread to the entire network of foundations giving to charter-school-related organizations. We do so by extending current work and focus on the broader institutional field that includes the interactions between major foundations, smaller foundations, and grantees over time. Moreover, we look to see, if such a field-wide convergence is present, whether there is evidence consistent with the institutional process of isomorphism in which low-status foundations match the giving strategy of higher status ones. Research Design We test for these dynamics using exponential random graph models (ERGMs), a hypothesis-testing framework for network analysis. More specifically, we analyze the funding ties among 809 foundations that gave grants to California charter schools and charter-school-related organizations between 2003 and 2014, as available through the Foundation Directory Online. We constructed multiyear windows to examine funding ties between foundations and recipients, using organizational characteristics, such as foundation type, foundation year, professionalization, foundation size, organizational type, and location, and endogenous features of the network as independent variables. Findings Results indicate centralization of giving over time, as larger and newer foundations began practicing more targeted giving and the most connected recipients were involved in a disproportionate number of funding ties. We also found evidence consistent with institutionalization, as foundations with professional staffs played a larger role in giving, and smaller foundations increasingly engaged in behavior similar to their larger peers over time. Finally, we found evidence for the consistent effect of propinquity: We observe co-funding and co-receiving ties between foundations and grantees in geographical proximity to each other. Conclusions This work examines the network dynamics of charter school philanthropic giving and provides evidence for the centralization and institutionalization of the field. In turn, this may create inequity in funding for charter schools because it may be more difficult for smaller or less ideologically popular organizations to penetrate the field. Policy makers should be aware of these forces and should take them into account when making budgetary and funding decisions.

scholarly journals The performance of permutations and exponential random graph models when analyzing animal networks

2020 ◽  
Vol 31 (5) ◽  
pp. 1266-1276 ◽  
Author(s):  
Julian C Evans ◽  
David N Fisher ◽  
Matthew J Silk
Keyword(s):  
Social Network ◽  
Random Graph ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Network Data ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph

Abstract Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

scholarly journals Bayesian exponential random graph models with nodal random effects

2016 ◽  
Vol 46 ◽  
pp. 11-28 ◽  
Author(s):  
S. Thiemichen ◽  
N. Friel ◽  
A. Caimo ◽  
G. Kauermann
Keyword(s):  
Random Effects ◽  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph
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