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.

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 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.

VARIETIES OF INTERGOVERNMENTAL ORGANIZATION MEMBERSHIPS AND STRUCTURAL EFFECTS IN THE WORLD TRADE NETWORK

2018 ◽  
Vol 21 (02) ◽  
pp. 1850001 ◽  
Author(s):  
ZHENGQI PAN
Keyword(s):  
World Trade ◽  
Bilateral Trade ◽  
Graph Model ◽  
General Purpose ◽  
Intergovernmental Organizations ◽  
Random Graph Model ◽  
Intergovernmental Organization ◽  
Exponential Random Graph ◽  
The World ◽  
Joint Membership

To what extent does joint membership in intergovernmental organizations (IGOs) matter for bilateral trade? How and under what conditions do the various types of IGOs — economic, socio-cultural and general purpose — influence bilateral trade between their members? How do complex interdependencies in world trade matter? Existing research tends to examine aggregate joint IGO memberships and has done little to analyze how specific types of IGO membership matter in trade. Using a detailed IGO dataset and a novel network analysis approach called the temporal exponential random graph model, I assess the importance of three main IGO types — economic, socio-cultural and general purpose — in helping members to establish major trading ties. The results provide support for general purpose and socio-cultural IGOs and point to the importance of network phenomena such as popularity, activity and transitivity effects. Moreover, joint economic IGO memberships exhibit slightly more complex relations with bilateral trade. A robustness test reveals that preferential trade agreements are significant in fostering trade, while the World Trade Organization and other economic IGOs such as development banks are not. This paper presents a nuanced way of analyzing IGOs and provides the impetus for the study of complex interdependencies in international trade.

Women trafficking networks: Structure and stages of women trafficking in five Dutch small-scale networks

2021 ◽  
pp. 147737082110531
Author(s):  
Tomáš Diviák ◽  
Jan Kornelis Dijkstra ◽  
Fenna van der Wijk ◽  
Indra Oosting ◽  
Gerard Wolters
Keyword(s):  
Law Enforcement ◽  
Graph Model ◽  
Small Scale ◽  
Law Enforcement Agencies ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
A Cell ◽  
Women Trafficking ◽  
The One ◽  
Networks Structure

In this study, we investigated the relation between the different stages of women trafficking (i.e. recruitment, entrance, accommodation, labor, and finance) and the structure of five criminal networks involved in women trafficking in the Netherlands ( Ns ranging from 6 to 15). On the one hand, it could be argued that for efficiency and avoidance of being detected by law enforcement agencies, the network structure might align with the different stages, resulting in a cell-structured network with collaboration between actors within rather than across stages. On the other hand, criminal actors might prefer to collaborate and rely on a few others, whom they trust in order to circumvent the lack of formal opportunities to enforce collaboration and agreements, resulting in a core-periphery network with actors also collaborating across stages. Results indicate that three of the five networks were characterized by a core-periphery structure, whereas the two other networks exhibit a mixture of both a cell-structured and core-periphery network. Furthermore, using an Exponential Random Graph Model (ERGM), we found that actors were likely to form ties with each other in the stages of recruitment, accommodation, and exploitation, but not in the stages of transport and finance.

scholarly journals Analyzing online search patterns of music festival tourists

2020 ◽  
pp. 135481662094544
Author(s):  
Juan D Montoro-Pons ◽  
Manuel Cuadrado-García
Keyword(s):  
Graph Model ◽  
Online Search ◽  
Music Festivals ◽  
Random Graph Model ◽  
Music Festival ◽  
Exponential Random Graph ◽  
Search Patterns ◽  
Patterns Of Behavior ◽  
Online Search Behavior ◽  
Tourism Flows

Music festivals, as cultural events that induce tourism flows, intermediate both the cultural and travel experience. The present study analyzes online search behavior of potential attenders to a music festival. We hypothesize that the search process reveals latent patterns of behavior of cultural tourists planning to attend music festivals. To this end, information from Google Trends on queries related to three popular music festivals is used to build a network of search topics. Based on it, alternative exponential random graph model specifications are estimated. Findings support the general result of mediated information flows: music festivals induce planning and traveling queries. However, differences relating to the specificities of the cultural event are also found, in particular those regarding what nodes or queries supply the network with more useful information.

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