Features of network interaction: the method of qualitative analysis and visualization online ego – networks

The method of qualitative analysis of Online Ego – networks visualization makes it possible to reveal the characteristic features of network interaction for Social Network Sites, to interpret the structural features of the Ego - networks, discover the main strategies used by participants to form network ties through online interactions. Analysis of 82 cases of Ego - networks has revealed different areas of community structure related to interests, ideologies and activities of the actors. At the same time, we have identified three main strategies of online ties formation: (1) formation of a single bond(2) formation of bonds forming a structurally homogeneous community(3) formation of bonds forming a heterogeneous structure with clearly defined communities. In addition, our results show that toward online networks from 4 to 1255 people: intimate network is about 5 people, a network of support and active contacts is about 8-10 and the average limit for the extended network - 150 -200 people.Метод качественного анализа визуализации Online Эго – сетей позволяет выявить характерные особенности сетевого взаимодействия на Сетевых Сайтах, интерпретировать особенности структуры Эго – сетей, обнаружить основные стратегии, используемые участниками online взаимодействий для формирования сетевых связей. Анализ 82 Эго – сетей позволил обнаружить структурные сообщества, связанные с интересами, мировоззрениями и деятельностью акторов. При этом мы выделили три основных стратегии образования связей: (1) формирование одиночных связи(2) формирование связей, образующих структурно гомогенные сообщества(3) формирование связей, образующих гетерогенную структуру с четко очерченными сообществами. Кроме того, наши результаты показывают, что для online сетей от 4 до 1255 человек: личная сеть составляет около 5 человек, сеть поддержки и активных контактов - около 8-10, а средний лимит для сети знакомых- 150 -200 человек

Understanding the interplay between social and spatial behaviour

According to personality psychology, personality traits determine many aspects of human behaviour. However, validating this insight in large groups has been challenging so far, due to the scarcity of multi-channel data. Here, we focus on the relationship between mobility and social behaviour by analysing trajectories and mobile phone interactions of ∼1000 individuals from two high-resolution longitudinal datasets. We identify a connection between the way in which individuals explore new resources and exploit known assets in the social and spatial spheres. We show that different individuals balance the exploration-exploitation trade-off in different ways and we explain part of the variability in the data by the big five personality traits. We point out that, in both realms, extraversion correlates with the attitude towards exploration and routine diversity, while neuroticism and openness account for the tendency to evolve routine over long time-scales. We find no evidence for the existence of classes of individuals across the spatio-social domains. Our results bridge the fields of human geography, sociology and personality psychology and can help improve current models of mobility and tie formation

Personality homophily and the local network characteristics of Facebook

Social networks are known to form on the basis of homophily, where nodes with some type of similar characteristics are more likely to be connected. Some of the most fundamental human characteristics are reflected by an individual's personality, which represents a persistent disposition governing a human's outlook and approach to diverse situations. While taking into account demographics of age and gender, we assess the extent to which personality homophily is evident in the local network features of Facebook. Using a large sample obtained from the MyPersonality dataset, we find that a range of network-based features correlate with personality facets of individuals. In particular, extraversion had a positive effect on an individual's network size, while neuroticism had a negative effect. Additionally, extraversion and openness were positively related to transitivity, which was moderated by gender. Finally, we found that conscientiousness, agreeableness and extraversion were homophilous: people with higher similarity on these facets were more strongly connected. This was additionally mediated by gender for agreeableness: personality similarity had an effect for male-only and mixed pairs, but not for female-only pairs. Personality similarity was also stronger among closed triangles, compared to open ones. These results support the idea that inherent attraction between individuals, on the basis of personality, drives the roles we play within our online social networks

Human mobility and social ties in context: from places to personality

Recent years saw an increasing proliferation of the use of digitally generated traces of data for understanding human behaviour. The quantitative understanding of social networks as well as patterns of human mobility benefited tremendously from these new sources of data. The main dynamics of both social networks and human mobility such as a propensity of humans for heterogeneous behaviour, how humans choose to explore new places, or the fact that both spheres are intrinsically linked are now fairly well understood. However, how various other factors mediate the observed dynamics is still relatively unknown, not least due to the difficulty in obtaining adequate data. Thus, for my thesis I focus on how a variety of factors---places, longer-term dynamics, the personality of individuals, or neighbourhoods---might be a driver of various aspects of social and mobility behaviour. I used data from the Copenhagen network study that tracked 847 students with smartphones and measured their social encounters as well as the locations they visited for a whole academic year. I further utilised a variety of methods for analysing the data ranging from applied machine learning over inferential statistics to social network analysis. Using this dataset, I found that the qualities of places were very informative for understanding future encounters between students, that the longer-term dynamics shaped both social and mobility behaviour, and that while personality had a significant effect on the observed regularity of behaviour, its effect was rather small

Psychological aspects of social communities

International audienceSocial Network Analysis has often focused on the structure of the network without taking into account the char- acteristics of the individual involved. In this work, we aim at identifying how individual differences in psychological traits affect the community structure of social networks. Instead of choosing to study only either structural or psychological properties of an individul, our aim is to exhibit in which way the psychological attributes of interacting individuals impacts the social network topology. Using psychological data from the myPersonality application and social data from Facebook, we confront the personality traits of the subjects to metrics obtained after applying the C3 community detection algorithm to the social neighborhood of the subjects. We observe that introverts tend to have less communities and hide into large communities, whereas extroverts tend to act as bridges between more communities, which are on average smaller and of varying cohesion

Psychological Aspects of Social Communities

International audienceSocial Network Analysis has often focused on the structure of the network without taking into account the char- acteristics of the individual involved. In this work, we aim at identifying how individual differences in psychological traits affect the community structure of social networks. Instead of choosing to study only either structural or psychological properties of an individul, our aim is to exhibit in which way the psychological attributes of interacting individuals impacts the social network topology. Using psychological data from the myPersonality application and social data from Facebook, we confront the personality traits of the subjects to metrics obtained after applying the C3 community detection algorithm to the social neighborhood of the subjects. We observe that introverts tend to have less communities and hide into large communities, whereas extroverts tend to act as bridges between more communities, which are on average smaller and of varying cohesion