Associative nature of event participation dynamics: a network theory approach

The affiliation with various social groups can be a critical factor when it comes to quality of life of each individual, making such groups an essential element of every society. The group dynamics, longevity and effectiveness strongly depend on group's ability to attract new members and keep them engaged in group activities. It was shown that high heterogeneity of scientist's engagement in conference activities of the specific scientific community depends on the balance between the numbers of previous attendances and non-attendances and is directly related to scientist's association with that community. Here we show that the same holds for leisure groups of the Meetup website and further quantify individual members' association with the group. We examine how structure of personal social networks is evolving with the event attendance. Our results show that member's increasing engagement in the group activities is primarily associated with the strengthening of already existing ties and increase in the bonding social capital. We also show that Meetup social networks mostly grow trough big events, while small events contribute to the groups cohesiveness.Comment: 16 pages, 6 figs + Supporting information 7 pages, 8 fig

Vlažnost i temperatura vazduha predviđaju broj objava na Tviteru u 10 zemalja – vremenske promene i LIWC psihološke kategorije

There are many indications that weather conditions influence human life and well-being. Some of these indicators, such as the influence of weather on human health, have been explored in detail. On the other hand, the influence of daily fluctuations of different meteorological variables on the human psychological state still remains unknown. We apply combined methods from statistics, psychology, machine learning, and complex networks theory to explore the influence of weather parameters on different psychological categories of Twitter users in ten different countries. Our results show that the temperature, pressure, and humidity are highly correlated with Twitter users’ activity, sense, and affect. Our comparative analysis for different countries shows that the strongest correlation was found for the USA, Italy, and Portugal, indicating differences between countries. However, our results show that the level of activity of Twitter users, described as Post Count, is strongly connected to changes in temperature and humidity in all countries. We use complex networks theory to explore these connections and differences between countries further. Our findings suggest that weather parameters can be used to predict Twitter users’ activity and psychological manifestations, which can be beneficial to marketing and advertising.Postoje mnoge indikacije da vremenske prilike utiču na živote i dobrobit ljudi. Neki od ovih pokazatelja, kao što je uticaj vremena na zdravlje ljudi, detaljno su istraženi. S druge strane, uticaj dnevnih fluktuacija različitih meteoroloških varijabli na psihičko stanje čoveka i dalje ostaje nepoznat. U ovom radu primenjujemo kombinovane metode iz statistike, psihologije, mašinskog učenja i složene teorije mreža da bismo istražili uticaj vremenskih parametara na različite psihološke kategorije korisnika Tvitera u deset različitih zemalja. Rezultati pokazuju da su temperatura, pritisak i vlažnost u korelaciji sa aktivnošcu, čulnim izražajima i afektom ́ kod korisnika Tvitera. Komparativna analiza među zemljama pokazuje da su najjače korelacije pronađene za SAD, Italiju i Portugal, što ukazuje na razlike između zemalja. Međutim, rezultati pokazuju da je broj tvitova korisnika društvene mreže Tviter povezan sa promenama temperature i vlažnosti u svim zemljama. Koristimo kompleksnu teoriju mreža da dalje istražimo ove veze i razlike između zemalja. Nalazi sugerišu da se vremenski parametri mogu koristiti za predviđanje aktivnosti i psiholoških manifestacija korisnika Tvitera, što može biti korisno za marketing i oglašavanje

Universal growth of social groups: empirical analysis and modeling

Social groups are fundamental elements of any social system. Their emergence and evolution are closely related to the structure and dynamics of a social system. Research on social groups was primarily focused on the growth and the structure of the interaction networks of social system members and how members' group affiliation influences the evolution of these networks. The distribution of groups' size and how members join groups has not been investigated in detail. Here we combine statistical physics and complex network theory tools to analyze the distribution of group sizes in three data sets, Meetup groups based in London and New York and Reddit. We show that all three distributions exhibit log-normal behavior that indicates universal growth patterns in these systems. We propose a theoretical model that combines social and random diffusion of members between groups to simulate the roles of social interactions and members' interest in the growth of social groups. The simulation results show that our model reproduces growth patterns observed in empirical data. Moreover, our analysis shows that social interactions are more critical for the diffusion of members in online groups, such as Reddit, than in offline groups, such as Meetup. This work shows that social groups follow universal growth mechanisms that need to be considered in modeling the evolution of social systems

Total number of attended events.

<p>Probability distributions <i>P</i>(<i>x</i>) of total number of participations <i>x</i>, for four Meetup groups. Solid line represents best fit to truncated power law distribution, <i>x</i>^−<i>α</i><i>e</i>^−<i>Bx</i>.</p

Summary of collected data for four selected Meetup groups.

<p><i>N</i>_<i>m</i> is total number of group members, <i>N</i>_<i>e</i> is total number of organised events.</p

Importance of event size for the network cohesiveness.

<p>Change of local network cohesiveness with removal of events according to their size (left) and temporal and random order (right). Abbreviations indicate order in which we remove events: <b>b</b>—from the largest to the smallest, <b>s</b>—from the smallest to the largest, <b>f</b>—from the first to the last and <b>r</b>—random.</p

Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks Approach

Various mathematical frameworks play an essential role in understanding the economic systems and the emergence of crises in them. Understanding the relation between the structure of connections between the system&rsquo;s constituents and the emergence of a crisis is of great importance. In this paper, we propose a novel method for the inference of economic systems&rsquo; structures based on complex networks theory utilizing the time series of prices. Our network is obtained from the correlation matrix between the time series of companies&rsquo; prices by imposing a threshold on the values of the correlation coefficients. The optimal value of the threshold is determined by comparing the spectral properties of the threshold network and the correlation matrix. We analyze the community structure of the obtained networks and the relation between communities&rsquo; inter and intra-connectivity as indicators of systemic risk. Our results show how an economic system&rsquo;s behavior is related to its structure and how the crisis is reflected in changes in the structure. We show how regulation and deregulation affect the structure of the system. We demonstrate that our method can identify high systemic risks and measure the impact of the actions taken to increase the system&rsquo;s stability

Node strength dependence on node degree.

<p>Dependence of average member’s strength 〈<i>s</i>〉 on her degree <i>q</i> in social network of significant links for considered groups.</p

Local cohesiveness of social networks of significant links.

<p>Evolution of local cohesiveness of members personal networks, measured by averaged non-weighted 〈<i>c</i>_<i>i</i>〉 and weighted clustering coefficients , with the number of events attended by the member <i>x</i>.</p