scholarly journals Tweeting #RamNavami: A Comparison of Approaches to Analyzing Bipartite Networks

2021 ◽  
pp. 227797522110180
Author(s):  
Michael T. Heaney
Keyword(s):  
Graph Model ◽  
Data Availability ◽  
Bipartite Networks ◽  
International Treaty ◽  
Random Graph Model ◽  
Affiliation Networks ◽  
Coalition Structures ◽  
Estimated Parameters ◽  
Exponential Random Graph ◽  
Event Model

Bipartite networks, also known as two-mode networks or affiliation networks, are a class of networks in which actors or objects are partitioned into two sets, with interactions taking place across but not within sets. These networks are omnipresent in society, encompassing phenomena such as student-teacher interactions, coalition structures and international treaty participation. With growing data availability and proliferation in statistical estimators and software, scholars have increasingly sought to understand the methods available to model the data-generating processes in these networks. This article compares three methods for doing so: (a) Logit (b) the bipartite Exponential Random Graph Model (ERGM) and (c) the Relational Event Model (REM). This comparison demonstrates the relevance of choices with respect to dependence structures, temporality, parameter specification and data structure. Considering the example of Ram Navami, a Hindu festival celebrating the birth of Lord Ram, the ego network of tweets using #RamNavami on 21April 2021 is examined. The results of the analysis illustrate that critical modelling choices make a difference in the estimated parameters and the conclusions to be drawn from them.

scholarly journals Modeling Network Dynamics

2020 ◽  
pp. 253-287
Author(s):  
David R. Schaefer ◽  
Christopher Steven Marcum
Keyword(s):  
Graph Model ◽  
Random Graph Model ◽  
Model Interpretation ◽  
Exponential Random Graph ◽  
Network Change ◽  
Dominance Interactions ◽  
Event Model ◽  
Network States ◽  
Data Requirements ◽  
Dynamic Phenomena

Given that social networks are inherently dynamic phenomena, characterizing their structure, precursors, and consequences can be improved by methodologies that incorporate such dynamism. This chapter discusses several longitudinal network modeling approaches that seek to understand the process of network change, on one hand, and to predict future network states, on the other. These include the relational event model (REM), exponential random graph model (ERGM), and stochastic actor-oriented model (SAOM). These models focus on different temporal resolutions and differentiate instantaneous events from relations with longer durations, among other distinctions. The chapter identifies commonalities and unique features of each model, both conceptually and via an application to a longitudinal network dataset of dominance interactions within a herd of Eurasian red deer. Throughout, the chapter emphasizes each modeling framework’s assumptions, data requirements, and parameter and model interpretation.

The configuration model

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Sequence ◽  
Graph Model ◽  
Network Models ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Configuration Model ◽  
Bipartite Networks ◽  
Small Component ◽  
Random Graph Model ◽  
Average Size

A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.

scholarly journals Symptom and Age Homophilies in SARS-CoV-2 Transmission Networks during the Early Phase of the Pandemic in Japan

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 499
Author(s):  
Ali Andalibi ◽  
Naoru Koizumi ◽  
Meng-Hao Li ◽  
Abu Bakkar Siddique
Keyword(s):  
Early Stage ◽  
Graph Model ◽  
Initial Response ◽  
Contact Tracing ◽  
Pcr Analysis ◽  
Exponential Random Graph Model ◽  
Transmission Networks ◽  
Random Graph Model ◽  
Healthcare Settings ◽  
Exponential Random Graph

Kanagawa and Hokkaido were affected by COVID-19 in the early stage of the pandemic. Japan’s initial response included contact tracing and PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. In this retrospective study, we analyzed publicly available COVID-19 registry data from Kanagawa and Hokkaido (n = 4392). Exponential random graph model (ERGM) network analysis was performed to examine demographic and symptomological homophilies. Age, symptomatic, and asymptomatic status homophilies were seen in both prefectures. Symptom homophilies suggest that nuanced genetic differences in the virus may affect its epithelial cell type range and can result in the diversity of symptoms seen in individuals infected by SARS-CoV-2. Environmental variables such as temperature and humidity may also play a role in the overall pathogenesis of the virus. A higher level of asymptomatic transmission was observed in Kanagawa. Moreover, patients who contracted the virus through secondary or tertiary contacts were shown to be asymptomatic more frequently than those who contracted it from primary cases. Additionally, most of the transmissions stopped at the primary and secondary levels. As expected, significant viral transmission was seen in healthcare settings.

scholarly journals A Case-Control Study to Identify Community Venues Associated with Genetically-clustered, Multidrug-resistant Tuberculosis Disease in Lima, Peru

2018 ◽  
Vol 68 (9) ◽  
pp. 1547-1555
Author(s):  
David P Bui ◽  
Eyal Oren ◽  
Denise J Roe ◽  
Heidi E Brown ◽  
Robin B Harris ◽  
...  
Keyword(s):  
Graph Model ◽  
Multidrug Resistant ◽  
Random Graph Model ◽  
Tuberculosis Transmission ◽  
Resistant Tuberculosis ◽  
Multidrug Resistant Tuberculosis ◽  
Exponential Random Graph ◽  
The Social ◽  
Community Segregation ◽  
Tuberculosis Disease

Abstract Background The majority of tuberculosis transmission occurs in community settings. Our primary aim in this study was to assess the association between exposure to community venues and multidrug-resistant (MDR) tuberculosis. Our secondary aim was to describe the social networks of MDR tuberculosis cases and controls. Methods We recruited laboratory-confirmed MDR tuberculosis cases and community controls that were matched on age and sex. Whole-genome sequencing was used to identify genetically clustered cases. Venue tracing interviews (nonblinded) were conducted to enumerate community venues frequented by participants. Logistic regression was used to assess the association between MDR tuberculosis and person-time spent in community venues. A location-based social network was constructed, with respondents connected if they reported frequenting the same venue, and an exponential random graph model (ERGM) was fitted to model the network. Results We enrolled 59 cases and 65 controls. Participants reported 729 unique venues. The mean number of venues reported was similar in both groups (P = .92). Person-time in healthcare venues (adjusted odds ratio [aOR] = 1.67, P = .01), schools (aOR = 1.53, P < .01), and transportation venues (aOR = 1.25, P = .03) was associated with MDR tuberculosis. Healthcare venues, markets, cinemas, and transportation venues were commonly shared among clustered cases. The ERGM indicated significant community segregation between cases and controls. Case networks were more densely connected. Conclusions Exposure to healthcare venues, schools, and transportation venues was associated with MDR tuberculosis. Intervention across the segregated network of case venues may be necessary to effectively stem transmission.

scholarly journals The drivers of regulatory networking: policy learning between homophily and convergence

2018 ◽  
Vol 39 (3) ◽  
pp. 443-464 ◽  
Author(s):  
Francesca P. Vantaggiato
Keyword(s):  
Regulatory Networks ◽  
Graph Model ◽  
Exponential Random Graph Model ◽  
Random Graph Model ◽  
Market Structures ◽  
Exponential Random Graph ◽  
Policy Frameworks ◽  
Regulatory Coordination ◽  
Regular Network ◽  
The Eu

AbstractThe literature on transnational regulatory networks identified interdependence as their main rationale, downplaying domestic factors. Typically, relevant contributions use the word “network” only metaphorically. Yet, informal ties between regulators constitute networked structures of collaboration, which can be measured and explained. Regulators choose their frequent, regular network partners. What explains those choices? This article develops an Exponential Random Graph Model of the network of European national energy regulators to identify the drivers of informal regulatory networking. The results show that regulators tend to network with peers who regulate similarly organised market structures. Geography and European policy frameworks also play a role. Overall, the British regulator is significantly more active and influential than its peers, and a divide emerges between regulators from EU-15 and others. Therefore, formal frameworks of cooperation (i.e. a European Agency) were probably necessary to foster regulatory coordination across the EU.

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