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.
The communication processes of knowledge creation represent a particular class of human dynamics where the expertise of individuals plays a substantial role, thus offering a unique possibility to ...study the structure of knowledge networks from online data. Here, we use the empirical evidence from questions-and-answers in mathematics to analyse the emergence of the network of knowledge contents (or tags) as the individual experts use them in the process. After removing extra edges from the network-associated graph, we apply the methods of algebraic topology of graphs to examine the structure of higher-order combinatorial spaces in networks for four consecutive time intervals. We find that the ranking distributions of the suitably scaled topological dimensions of nodes fall into a unique curve for all time intervals and filtering levels, suggesting a robust architecture of knowledge networks. Moreover, these networks preserve the logical structure of knowledge within emergent communities of nodes, labeled according to a standard mathematical classification scheme. Further, we investigate the appearance of new contents over time and their innovative combinations, which expand the knowledge network. In each network, we identify an innovation channel as a subgraph of triangles and larger simplices to which new tags attach. Our results show that the increasing topological complexity of the innovation channels contributes to network's architecture over different time periods, and is consistent with temporal correlations of the occurrence of new tags. The methodology applies to a wide class of data with the suitable temporal resolution and clearly identified knowledge-content units.
Collective knowledge as a social value may arise in cooperation among actors whose individual expertise is limited. The process of knowledge creation requires meaningful, logically coordinated ...interactions, which represents a challenging problem to physics and social dynamics modeling. By combining two-scale dynamics model with empirical data analysis from a well-known Questions &Answers system Mathematics, we show that this process occurs as a collective phenomenon in an enlarged network (of actors and their artifacts) where the cognitive recognition interactions are properly encoded. The emergent behavior is quantified by the information divergence and innovation advancing of knowledge over time and the signatures of self-organization and knowledge sharing communities. These measures elucidate the impact of each cognitive element and the individual actor's expertise in the collective dynamics. The results are relevant to stochastic processes involving smart components and to collaborative social endeavors, for instance, crowdsourcing scientific knowledge production with online games.
In many complex systems, self-organised criticality (SOC) provides a mechanism for the diversity of spatiotemporal scales that optimises the system’s response to omnipresent driving forces. ...Signatures of SOC are increasingly more evidenced in collective social behaviours. However, the spontaneous occurrence of critical states and their role in maintaining the system’s functional properties still need to be better understood; the reason can be related to the complexity of human interactions and the ubiquitous presence of cycles in social dynamics. In this work, we shed new light on these issues based on a critical survey and the extensive data analysis of online social dynamics. Firstly, we highlight prominent features of human activity patterns, conditioned by circadian cycles and content-related interactions, that can affect the course of the dynamics from the elemental to the global scale. We then analyse the prototypal time series of emotion-driven communications in the online social network MySpace to demonstrate the coexistence of SOC states with the modulated cyclical trends. Precisely, we determine avalanches of emotional comments exhibiting multifractal scaling, scale-invariant inter-avalanching behaviours and temporal correlations coexist with the cyclical trends of broad singularity spectra. We demonstrate that similar multi-harmonic cycles occur in entirely different datasets, particularly the negative emotion-driven Diggs and the infection-rate data from recent epidemics. Our results reveal the dynamical regime where the modulated cycles coexist with self-organised critical states; in contrast, in the cycles-dominated regime, exemplified by the infection time series, the nature of collective dynamics remains hidden behind the cycle modulation.
•Avalanches of emotional comments in online social systems are multifractal.•In online systemsmodulated cycles coexist with self-organised critical states.•In epidemics spreading collective dynamics remains hidden behind the cycle modulation.
Participation in conferences is an important part of every scientific career. Conferences provide an opportunity for a fast dissemination of latest results, discussion and exchange of ideas, and ...broadening of scientists' collaboration network. The decision to participate in a conference depends on several factors like the location, cost, popularity of keynote speakers, and the scientist's association with the community. Here we discuss and formulate the problem of discovering how a scientist's previous participation affects her/his future participations in the same conference series. We develop a stochastic model to examine scientists' participation patterns in conferences and compare our model with data from six conferences across various scientific fields and communities. Our model shows that the probability for a scientist to participate in a given conference series strongly depends on the balance between the number of participations and non-participations during his/her early connections with the community. An active participation in a conference series strengthens the scientist's association with that particular conference community and thus increases the probability of future participations.
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’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’ 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’ 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’ inter and intra-connectivity as indicators of systemic risk. Our results show how an economic system’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’s stability.
Predicting the evolution of the current epidemic depends significantly on understanding the nature of the underlying stochastic processes. To unravel the global features of these processes, we ...analyse the world data of SARS-CoV-2 infection events, scrutinising two 8-month periods associated with the epidemic’s outbreak and initial immunisation phase. Based on the correlation-network mapping, K-means clustering, and multifractal time series analysis, our results reveal several universal patterns of infection dynamics, suggesting potential predominant drivers of the pandemic. More precisely, the Laplacian eigenvectors localisation has revealed robust communities of different countries and regions that break into clusters according to similar profiles of infection fluctuations. Apart from quantitative measures, the immunisation phase differs significantly from the epidemic outbreak by the countries and regions constituting each cluster. While the similarity grouping possesses some regional components, the appearance of large clusters spanning different geographic locations is persevering. Furthermore, characteristic cyclic trends are related to these clusters; they dominate large temporal fluctuations of infection evolution, which are prominent in the immunisation phase. Meanwhile, persistent fluctuations around the local trend occur in intervals smaller than 14 days. These results provide a basis for further research into the interplay between biological and social factors as the primary cause of infection cycles and a better understanding of the impact of socio-economical and environmental factors at different phases of the pandemic.
Knowledge-sharing communities are fundamental elements of a knowledge-based society. Understanding how different factors influence their sustainability is of crucial importance. We explore the role ...of the social network structure and social trust in their sustainability. We analyze the early evolution of social networks in four pairs of active and closed Stack Exchange communities on topics of physics, astronomy, economics, and literature and use a dynamical reputation model to quantify the evolution of social trust in them. In addition, we study the evolution of two active communities on mathematics topics and two closed communities about startups and compare them with our main results. Active communities have higher local cohesiveness and develop stable, better-connected, trustworthy cores. The early emergence of a stable and trustworthy core may be crucial for sustainable knowledge-sharing communities.
We find that nitrogen plasma treatment of micro/nanofibrillated cellulose films increases wettability of the surface by both liquid polar water and nonpolar hexadecane. The increased wetting effect ...is more pronounced in the case of polar liquid, favouring the use of plasma treated micro/nanofibrillated cellulose films as substrates for a range of inkjet printing including organic-based polar-solvent inks. The films were formed from aqueous suspensions of progressively enzymatic pretreated wood-free cellulose fibres, resulting in increased removal of amorphous species producing novel nanocellulose surfaces displaying increasing crystallinity. The mechanical properties of each film are shown to be highly dependent on the enzymatic pretreatment time. The change in surface chemistry arising from exposure to nitrogen plasma is revealed using X-ray photoelectron spectroscopy. That both polar and dispersive surface energy components become increased, as measured by contact angle, is also linked to an increase in surface roughness. The change in surface free energy is exemplified to favour the trapping of photovoltaic inks.
The essence of the stochastic processes behind the empirical data on infection and fatality during pandemics is the complex interdependence between biological and social factors. Their balance can be ...checked on the data of new virus outbreaks, where the population is unprepared to fight the viral biology and social measures and healthcare systems adjust with a delay. Using a complex systems perspective, we combine network mapping with K-means clustering and multifractal detrended fluctuations analysis to identify typical trends in fatality rate data. We analyse global data of (normalised) fatality time series recorded during the first two years of the recent pandemic caused by the severe acute respiratory syndrome coronavirus 2 as an appropriate example. Our results reveal six clusters with robust patterns of mortality progression that represent specific adaptations to prevailing biological factors. They make up two significant groups that coincide with the topological communities of the correlation network, with stabilising (group g1) and continuously increasing rates (group g2). Strong cyclic trends and multifractal small-scale fluctuations around them characterise these patterns. The rigorous analysis and the proposed methodology shed more light on the complex nonlinear shapes of the pandemic’s main characteristic curves, which have been discussed extensively in the literature regarding the global infectious diseases that have affected humanity throughout its history. In addition to better pandemic preparedness in the future, the presented methodology can also help to differentiate and predict other trends in pandemics, such as fatality rates, caused simultaneously by different viruses in particular geographic locations.
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