network science
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2022 ◽  
Vol 69 (02) ◽  
pp. 1
Author(s):  
Sinan G Aksoy ◽  
Aric Hagberg ◽  
Cliff A Joslyn ◽  
Bill Kay ◽  
Emilie Purvine ◽  
...  
Keyword(s):  

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Hendrik Nunner ◽  
Arnout van de Rijt ◽  
Vincent Buskens

AbstractA twenty-year-old idea from network science is that vaccination campaigns would be more effective if high-contact individuals were preferentially targeted. Implementation is impeded by the ethical and practical problem of differentiating vaccine access based on a personal characteristic that is hard-to-measure and private. Here, we propose the use of occupational category as a proxy for connectedness in a contact network. Using survey data on occupation-specific contact frequencies, we calibrate a model of disease propagation in populations undergoing varying vaccination campaigns. We find that vaccination campaigns that prioritize high-contact occupational groups achieve similar infection levels with half the number of vaccines, while also reducing and delaying peaks. The paper thus identifies a concrete, operational strategy for dramatically improving vaccination efficiency in ongoing pandemics.


2022 ◽  
pp. 207-232
Author(s):  
Elie Alhajjar
Keyword(s):  

Author(s):  
Lionel Alangeh Ngobesing ◽  
Yılmaz Atay

Abstract: In network science and big data, the concept of finding meaningful infrastructures in networks has emerged as a method of finding groups of entities with similar properties within very complex systems. The whole concept is generally based on finding subnetworks which have more properties (links) amongst nodes belonging to the same cluster than nodes in other groups (A concept presented by Girvan and Newman, 2002). Today meaningful infrastructure identification is applied in all types of networks from computer networks, to social networks to biological networks. In this article we will look at how meaningful infrastructure identification is applied in biological networks. This concept is important in biological networks as it helps scientist discover patterns in proteins or drugs which helps in solving many medical mysteries. This article will encompass the different algorithms that are used for meaningful infrastructure identification in biological networks. These include Genetic Algorithm, Differential Evolution, Water Cycle Algorithm (WCA), Walktrap Algorithm, Connect Intensity Iteration Algorithm (CIIA), Firefly algorithms and Overlapping Multiple Label Propagation Algorithm. These al-gorithms are compared with using performance measurement parameters such as the Mod-ularity, Normalized Mutual Information, Functional Enrichment, Recall and Precision, Re-dundancy, Purity and Surprise, which we will also discuss here.


PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0261811
Author(s):  
Nicholas Rabb ◽  
Lenore Cowen ◽  
Jan P. de Ruiter ◽  
Matthias Scheutz

Understanding the spread of false or dangerous beliefs—often called misinformation or disinformation—through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual’s set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. Some computational social science modelers analyze agent-based models where individuals do have simulated cognition, but they often lack the strengths of network science, namely in empirically-driven network structures. We introduce a cognitive cascade model that combines a network science belief cascade approach with an internal cognitive model of the individual agents as in opinion diffusion models as a public opinion diffusion (POD) model, adding media institutions as agents which begin opinion cascades. We show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. We conduct an analysis of the cognitive cascade model with our simple cognitive function across various graph topologies and institutional messaging patterns. We argue from our results that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID-related public opinion polls, and that the model dynamics lend insights as to how to address the spread of problematic beliefs. The overall model sets up a framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of disinformation and “alternative facts.”


2022 ◽  
pp. 119-132
Author(s):  
Tomáš Gajdošík ◽  
Marco Valeri

Tourism destinations can be considered as complex systems of interrelated and interdependent stakeholders. The complexity and limited power of influencing the number of stakeholders resulted in network approach to tourism destination governance. This approach is considered both theoretically and practically as a tool for strengthening its sustainable competitiveness, fostering innovation and knowledge sharing. Although the network analysis of tourism destinations has gained a significant attention in recent years, the complex understanding of its contribution to smart development is still missing. The aim of this chapter is to create a framework for smart approach in destination governance using the network science perspective. The chapter provides insights in using network analysis for strengthening the tourism destination governance. The chapter uses a case study methodology on two mature tourism destinations, providing an example of the use of network analysis for destination governance strengthening.


2022 ◽  
Vol 48 (17) ◽  
Author(s):  
Joao Meidanis
Keyword(s):  

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Zubaida Jastania ◽  
Rabeeh Ayaz Abbasi ◽  
Mohammad Ahtisham Aslam ◽  
Tariq Jamil Saifullah Khanzada ◽  
Khawaja Moyeezullah Ghori
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