Abstract: Brain, as a self-organized critical system, is the most complex system in nature, and has been always attractive for many scientists. Brain functionality depends on various features, the most important of which is the topological structure of the neuronal network. Several research studies found that the small-world property has a substantial impact on optimizing the brain function. But this is still a questionable finding, which necessitates more investigation for better understanding of the brain complex network. In this study, we investigate the importance of the effect of high connectivity on the brain network, and also on its self-organization property. In this respect, we simulate the brain structure using complex network theory, along with a spreading model for the brain dynamics. We also investigate the effect of heterogeneity of the connectivity by generating different types of networks, such as Erdos-Renyi, Small-World and Scale-Free networks. We show that by increasing the average degree, the avalanche size/duration distributions approach into the universal critical power-law behavior with mean-field solutions, independent of the network topology. On the other hand, we also show that for a network with small average degree, in spite of possessing the small-world property, the system is far from the critical state. We conclude that one of the most important factors affecting the performance of self-organization of the brain may be high connectivity, which is in agreement with the observations.

Abstract: Biology forms a complex system with a nested hierarchical structure and clear parent-child relationships, where a collective interaction of children defines properties of a parent. For example, interaction of molecular machinery defines properties of cells, whereas interaction of cells defines the properties of the whole organism. Nowadays, research of all the levels of biological organisation, from molecules to ecosystems, benefits from application of computational modelling. Arguably, joining models from different levels would allow us to understand biology much better. However, in practice, it is hard for an individual, or even a group, to develop a holistic predictive model of at least several levels of biological organisation due to complexity at each individual level. Furthermore, the diversity of implementation methods, algorithms, and programming languages that are used to create biological models makes it hard to cooperate and share. Here I present a minimalistic C++ library, which purpose is to combine modelling efforts across the levels of biological organisation, into a single computational environment. It exploits a generic programming approach to create a recursive, hierarchical template, which can be populated with user-defined models. The structure of the template imposes strict encapsulation rules on the models, while leaving developers a free choice of implementation methods, thus promoting cooperation and resolving compatibility conflicts between the models.

Abstract: Many systems in Nature can be described by a combination of local reaction rules - representing the creation or destruction of entities - with a diffusion process - the migration of entities. Reaction-diffusion equations are therefore widely used to model such systems. In accordance with the growing interest for network science, we consider these equations when evolving on complex networks. We analyze them from the self-organization point of view, and more precisely for Turing instabilities which can explain the emergence of patterns under the effect of diffusion. The reach of Turing's study now goes far beyond the original biological setting, as it is now widespread in physics, chemistry, ecology, social systems, just to name a few. We present our study of a two-species activator-inhibitor model, accounting for the presence of a time-delay impacting all the processes in the nodes of the network, possibly due to a wait-then-act strategy or simply the consequence of inertia in the concentration sites [1,2]. Our approach is based on a spectral analysis and on the properties of the scalar Lambert W-function, allowing us to obtain a complete description of the stability of the system. We explore the role of the different variables in the parameters space, and predict the formation of either stationary patterns, or Turing-like waves. With our 5 minutes presentation, we hope to put three main points across. First, why it came to us to incorporate a time-delay in a regular reaction-diffusion model. Second, suggest both a taste of the mathematical challenge this represents, and how we picked our way through it. And finally, give a feeling for the role of every parameters in the self-organizing behavior of the model, with a particular focus on the time-delay. References [1] Petit et al, EPL 111, 58002 (2015) [2] Petit et al, arXiv 1603.07122 (2016)

Abstract: For this IGNITE presentation at CCS2016, Amsterdam-based Slow Research Lab presents the material innovations of designer Maria Blaisse and how they address human-scale challenges in modeling/simulating complexity and fluid interface design. Blaisse’s work stems from a tradition of designers working at the intersection of form, mathematics, and research on nonlinear dynamics in nature (e.g., Buckminster Fuller’s “synergetics,” Paul Schatz’ “polysomatic forms”), while dovetailing with material/physical design theory that has influenced programming languages and object-oriented interfaces (e.g., Christopher Alexander’s “deep geometrical structures,” “generative grammar,” etc.). Blaisse’s current research trajectory into the formal, relational, and energy-generating potentialities of bamboo has yielded a series of flexible structures that give tangible expression to humanity’s possible near-future advances in infrastructure (architecture, transport, energy), interface design (fluid, human-scale, interactive mathematical modeling) and socio-ecological systems (systems resilience simulation, environmental bioenergetics). Format for this presentation: Slow Research Lab director Carolyn Strauss will deliver a three minute oral analysis of Blaisse’s work in relation to interaction design thinking strategies for modeling and simulating complex systems, accompanied by a performance demonstration of the bamboo structures by movement artist and complexity researcher Siobhán K. Cronin. (This video exhibits possibilities for the performance: http://bit.ly/1rCKp4j) Attendees of the presentation will gain insight into the role that design can play in expanding the frontiers of complexity research. The presentation is certain to amaze and inspire, literally igniting conversation and new pathways of interdisciplinary collaboration.

Abstract: How do multiple topics compete for the limited resource of user attention in social networks? What are the main paths in the process of information diffusion? Although much of the previous work had focused on the study of topics competition in social networks, most of them were model-based analysis, which cannot describe the temporal and spatial behaviors of users in the real world. In this work, we analyze the information sharing logs from WeChat – the largest instant messaging communication service in China – with the goal of understanding the diffusion processes. We analyzed the role of social groups and the underlying social structure in the topics competition, and presented a timeline visualization by compositing ThemeRiver with storyline style visualization. The increase and decrease of competitiveness of the topics are shown in this work.

Abstract: Social influence has been an important topic of research in the social sciences. Recently, thanks to the huge amounts of digital data produced by our daily activities, research on how much our behaviour is driven by others has produced new studies with non-trivial results. We use Last.fm's song-listen data to quantify social influence on music listenership around live events. We study live events performed by “Hyped” (i.e. trending) and “Top” (i.e. most popular) artists. We analyse how listenership changes around the time of the live event both for users who attended the event, and more importantly, for those who did not, but are friends with someone who attended. The analysis in this paper uses publicly available data from the music website Last.fm. We use three distinct types of data: (1) event attendance, (2) track listens, and (3) the Last.fm friends network. We extracted all live events in 2013 and 2014 by the most popular Hyped and Top artists. We tracked listenership of 1.3 million users over a two month time horizon—with one month of listenership data prior to the attendance of an event and one month of listenership data after the attendance of the event. Our analysis show that social influence exist in both cases of hyped and top artists but it is much stronger in the latter case. We also show how the number of "friends" would increase the effects of social influence. We argue how complex social contagion is the right model to explain these observations.