Evolution of social networks: the example of obesity

Abstract

The present paper deals with the effect of the social transmission of nutrition habits in a social and biological age-dependent context on obesity, and accordingly on type II diabetes and among its complications, the neurodegenerative diseases. The evolution of social networks and inside a network the healthy weight of a person are depending on the context in which this person has contacts and exchanges concerning his alimentation, physical activity and sedentary habits, inside the dominant social network in which the person lives (e.g., scholar for young, professional for adult, home or institution for elderly people). Three successive steps of evolution will be considered for social networks (like for neural one's): initial random connectivity, destruction and consolidation of links following a new transition rule called homophilic until an asymptotic architectural organization and configuration of states. The application of such a network dynamics concerns the sequence overweight/obesity/type II diabetes and neurodegenerative diseases.

Characterizing Social Interaction in Tobacco-Oriented Social Networks: An Empirical Analysis

“…The analysis of user interaction reveals the user interaction patterns in the sale campaigns, which makes it possible to differentiate the sale campaigns from other tobacco-related content. Meanwhile, the research progress in other field such as the research about obesity 42 43 44 could be applied to tobacco-related research to reveal the spreading and evolution of tobacco-related social works.…”

Phase transitions in stochastic non-linear threshold Boolean automata networks on Z2: The boundary impact

“…Since decades, researches in discrete mathematics and fundamental computer science have put the emphasis on the modelling abilities of automata networks concerning interaction networks. In particular, since their introduction in the works of McCulloch and Pitts [31] and Kauffman [29,28], Boolean automata networks (bans for short) have been at the centre of numerous studies in the field of biological networks modelling, like neural networks [20,24,25,21,9,8], genetic regulation networks [30,43,44,41,32,4,36] and more recently social networks [16,12]. This can be easily explained by their very high level of abstraction that makes them ideal objects to capture formally the essence of interactions and to focus on qualitative aspects of their dynamics (e.g., the information transmissions).…”

Smart Homes and Sensors for Surveillance and Preventive Education at Home: Example of Obesity

“…The psychological factors identified in the literature are mood welfare [63], self-assessment [78,79], anxiety [80], stress [81], self-esteem [82], impulsivity [27,45,83] and self-perception of body weight [10,25,35,41,63,82,[84][85][86][87][88][89][90]. In [27], the authors conclude that personality traits are associated with weight gain, and weight gain can also be associated with a change in personality.…”

“…In [86], it is shown that an accurate weight perception in overweight people was associated with an increased frequency of disordered eating habits. In [89], the authors have found that the highest levels of perceived weight were obtained in individuals who are underweight and that older men and younger women classified themselves properly, as well as men without children and also women with children.…”