Explorations on Influential node Identification and Gender Prediction in Dolphin Network through Social Network Analysis

AbstractThe aims: (i) analyze connectivity between subgroups of university students, (ii) assess which bridges of relational contacts are essential for connecting or disconnecting subgroups and (iii) to explore the similarities between the attributes of the subgroup nodes in relation to the pandemic context. During the COVID-19 pandemic, young university students have experienced significant changes in their relationships, especially in the halls of residence. Previous research has shown the importance of relationship structure in contagion processes. However, there is a lack of studies in the university setting, where students live closely together. The case study methodology was applied to carry out a descriptive study. The participation consisted of 43 university students living in the same hall of residence. Social network analysis has been applied for data analysis. Factions and Girvan–Newman algorithms have been applied to detect the existing cohesive subgroups. The UCINET tool was used for the calculation of the SNA measure. A visualization of the global network will be carried out using Gephi software. After applying the Girvan–Newman and Factions, in both cases it was found that the best division into subgroups was the one that divided the network into 4 subgroups. There is high degree of cohesion within the subgroups and a low cohesion between them. The relationship between subgroup membership and gender was significant. The degree of COVID-19 infection is related to the degree of clustering between the students. College students form subgroups in their residence. Social network analysis facilitates an understanding of structural behavior during the pandemic. The study provides evidence on the importance of gender, race and the building where they live in creating network structures that favor, or not, contagion during a pandemic.

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Proximity patterns in water buffaloes' activities on pasture

Archives Animal Breeding ◽

10.5194/aab-63-19-2020 ◽

2020 ◽

Vol 63 (1) ◽

pp. 19-29

Author(s):

Eleni T. Tsiobani ◽

Maria D. Yiakoulaki ◽

Nikolaos D. Hasanagas ◽

Ioannis E. Antoniou

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Social Structure ◽

Management Practices ◽

Local Network ◽

Age And Gender ◽

Water Buffaloes ◽

And Gender ◽

The Way

Abstract. Water buffaloes are considered social animals and perform several activities on pasture, such as grazing, moving, standing, ruminating, wallowing, lying, and drinking. However, the way these animals form their social structure in the herd during each one of these activities is still unknown. Literature for water buffaloes has focused mainly on their productive characteristics, impact of grazing on wetlands and behavior during grazing but failed to address the way these animals form their social organization during their activities on pasture. In this study, the tools of social network analysis are used to analyze, detect, and depict the proximity patterns in water buffaloes' activities on pasture and the effect of their age and gender on them. We describe and interpret a series of global and local network indices, and show that the water buffaloes differentiate their social structure in their activities on pasture and that the animals' age and gender affect their interacting patterns, and provide a framework for the application of social network analysis on grazing animals' social behavioral studies. We expect that this framework could be used in future research to provide information regarding the social structure of other kinds of animals that graze in different forage and climatic environments and help animal breeders to improve their management practices.

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#METOO AND INTERSECTIONALISM: "RADICAL COMMUNITY HEALING" OR "VOYEURISTIC TRAUMA PORN?"

AoIR Selected Papers of Internet Research ◽

10.5210/spir.v2019i0.11044 ◽

2019 ◽

Vol 2019 ◽

Author(s):

Verity Trott

Keyword(s):

Sexual Abuse ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

White Women ◽

Large Data ◽

Data Set ◽

Mainstream Media ◽

And Gender ◽

The Many

In October 2017, millions of people reflected on their experiences of sexual abuse and harassment, publicly sharing their testimonials in an expression of global vulnerability using the hashtag #MeToo. Many of the tweets portrayed the angst and distress individuals experienced in their decision to participate, indicating the psychological costs of engaging with #MeToo. Further, some tweets expressed frustration at the re-appropriated nature of the campaign and the collective feeling of an “intersectional betrayal” by white women and feminists who dominated the mainstream media reporting of the movement. This research foregrounds the intersectional concerns that result from the scale and reach of the millions of testimonials suspended online that constitute the #MeToo movement. It highlights how the many stories that have circulated the online sphere obscure the absence and recognition of marginalised women and those who are already more vulnerable in regards to experiencing sexual assault. The paper adopts an intersectional framework, as conceptualised by Crenshaw (1991), to further an understanding of how race, class, and gender collide and how subordination can be reproduced within feminist protests. Drawing on a large data set of tweets, this research combines content, discourse and social network analysis to examine the narratives related to participation. The paper highlights the experiences and reflections of users who self-identified as queer, disabled, or a person of colour within their tweets. A social network analysis is also used to visualise a snapshot of the affective publics that arose at the beginning and to illustrate how systems of oppression converge.

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