Simulating Networks with Heavy-tailed Degree Distributions

The ability to simulate networks accurately and efficiently is of growing importance as many aspects of life become increasingly interconnected. Various disciplines (sociology, biology, computer science, physics) have found that utilizing network theory allows them to answer many of the questions in their respective research areas. Over time, networks in almost all disciplines have gotten larger and more interconnected making simulation and analysis an increasingly difficult task; however, there is one class of networks which pose particularly difficult computational challenges. Networks with heavy–tailed degree sequences have always been a challenge to simulate efficiently since they do not meet the assumptions of many popular network simulation methods and the methods which are valid have a problematically long run time. As networks have gotten larger the cost of using the inefficient methods has become too great. We begin with a full exploration of why sequences drawn from heavy–tailed degree distributions fail to be graphic so often. After fully describing the graphicality failure, two methods for increasing the efficiency of generating graphic sequences when using heavy-tailed degree distributions are presented. The first is a classic solution which employs a modified rejection sampler that increases the graphicality probability. The second is a novel approach based on altering nongraphic sequences into graphic sequences. There is also an exploration of the viability of using a new network decomposition for the simulation of networks with heavy–tailed degree distributions

Elections in the time of covid-19 : the triple crises around Malawi’s 2020 presidential elections

Abstract: In June 2020, in the midst of the Covid-19 pandemic, Malawians went to the polls and voted to replace the incumbent government. Much like other natural disasters, the Covid-19 pandemic and accompanying economic and political shocks had the potential to shake voters’ confidence in the government, reduce turnout, and/or reduce support for the incumbent if voters associated them with the ills of the pandemic. In this paper, we examine the extent to which the Coronavirus pandemic influenced Malawi’s 2020 elections. We consider how fear of infection and economic distress affected citizens’ trust and confidence in President Mutharika’s government, their willingness to turn out to vote, and their choices at the polls using data collected pre- and post-Covid. We find that fears about the virus and its economic impact did influence trust and confidence in the government to handle Covid but had little to no effect on either abstention or vote choice

Simulating Networks with Heavy-tailed Degree Distributions

The ability to simulate networks accurately and efficiently is of growing importance as many aspects of life become increasingly interconnected. Various disciplines (sociology, biology, computer science, physics) have found that utilizing network theory allows them to answer many of the questions in their respective research areas. Over time, networks in almost all disciplines have gotten larger and more interconnected making simulation and analysis an increasingly difficult task; however, there is one class of networks which pose particularly difficult computational challenges. Networks with heavy–tailed degree sequences have always been a challenge to simulate efficiently since they do not meet the assumptions of many popular network simulation methods and the methods which are valid have a problematically long run time. As networks have gotten larger the cost of using the inefficient methods has become too great. We begin with a full exploration of why sequences drawn from heavy–tailed degree distributions fail to be graphic so often. After fully describing the graphicality failure, two methods for increasing the efficiency of generating graphic sequences when using heavy-tailed degree distributions are presented. The first is a classic solution which employs a modified rejection sampler that increases the graphicality probability. The second is a novel approach based on altering nongraphic sequences into graphic sequences. There is also an exploration of the viability of using a new network decomposition for the simulation of networks with heavy–tailed degree distributions

Zambian election panel survey: Dataset of responses before, near, and after 2021 elections

The Zambian Election Panel Survey (ZEPS) data allows for analysis of voter perceptions and choices in the August 2021 elections – and how these were affected by the tactics of the competing parties and candidates. The panel design provides an opportunity to study how, when and why former supporters of incumbent president Lungu ‘defected’ to support his rival, Hakainde Hichilema (HH) in 2021. This multistage panel survey, the first-of-its-kind in Africa, was implemented in three rounds; June 5 – July 5 (R1: n = 1665), July 15 – Aug 11 (R2: n = 1508), and Aug 25 – Oct 3 (R3: n = 1272). These time periods correspond to the early campaign period, late in the campaign period and immediate post-election period, respectively. The survey was conducted over the phone. Responses were disproportionately from urban/peri-urban voters in Central and Lusaka provinces and rural voters in Eastern and Muchinga provinces. 1764 unique responses were collected using SurveyToGo software from Dooblo. 1210 responses were collected for all three rounds

Modelling and Simulation of the Formation of Social Networks

Social networking has been a feature of human society. From the early hunter-gatherer tribes, medieval guilds, the twentieth century workplaces, up to online entities like Facebook and Twitter, it is difficult to think of a time or place where all people did not belong to at least one cooperative group. It follows that social network formation has been studied extensively in the past decades and will continue to be a popular area of research. Past research has primarily confined itself to considering cases in which new members are introduced into the networks by making a constant number of connections to those who are already present in the networks. Our study aims to fill the glaring gap in the variety of network formation modelling. Most notably, we want to consider scenarios in which the number of connections new members make to those already present in the networks is determined by chance. More specifically, the number of connections made to existing members when a new one is introduced into the network is characterized by a positive integer-valued random variable. The objective of the study is to determine the distribution of degree of a node in this kind of social networks. It is determined that the node degree distribution is a mixture of geometric distributions. Three numerical examples are provided in the study to demonstrate the validity of our findings