Dynamics of Multilevel Complex Systems  (DMC) Session 2

Abstract: When assessing the security or robustness of a complex system, including the fact that many nodes may fail together is essential. Though complex network studies typically assume that nodes are identical with respect to their vulnerability to failure or attack, this is often inaccurate.Surprisingly, this heterogeneity can be utilized to improve the system's functionality using a new ``color-avoiding percolation'' theory.We illustrate this with a new topological approach to cybersecurity.If there are many eavesdroppers, each tapping many nodes, we propose to split the message, and transmit each piece on a path that avoids all the nodes which are vulnerable to one of the eavesdroppers.Our theory determines which nodes can securely communicate and is applicable to a wide range of systems, from economic networks to epidemics Close

Abstract: The project TOPDRIM envisioned a new mathematical and computational framework based on topological data analysis for probing data spaces and extracting manifold hidden relations (patterns) that exist among data. While pursuing this objective, a general program aiming to construct an innovative methodology to perform data analytics has been devised. This program proposes the realization of a Field Theory of Data starting from topological data analysis, passing through field theory and returning an automaton as a recognizer of the data language. TOPDRIM contributed mainly to the first stage of the program by giving evidence that topological data analysis is a viable tool to tame the wild collection of data and to detect changes in complex networks. However, TOPDRIM already went beyond the concept of networks by considering instead simplicial complexes, which allow the study of n-dimensional objects (n>=2). An alternative approach to machine learning has been put forward, where data mining starts without receiving any initial input. Close

Abstract: Multilayer infrastructure is often interdependent, with nodes in one layer depending on nearby nodes in another layer to function. The links in each layer are often of limited length, due to the construction cost of longer links. Here, we model such systems as a multiplex network, in which each layer has links of characteristic geographic length. This is equivalent to a system of interdependent spatially embedded networks in which the connectivity links are constrained in length but varied while the length of the dependency links is always zero. We find two distinct percolation transition behaviors depending on the characteristic length of the links. When this value is longer than a certain critical value, abrupt, first-order transitions take place, while for shorter values the transition is continuous. Close

Abstract: In this talk, we will give a topological characterization of functional and behavioural features of complex systems. In particular we propose an interpretation of languages of regular expressions as the outcome of global topological features of the space intrinsically generated by the formal representation of processes constrained over the space. Our goal is a new scheme, (in the sense of Grothendieck) allowing for a new characterization of regular expressions and the study of a different axiomatic structure, analogous to Kleene algebras, but encompassing non-deterministic process interpretation. Close

Abstract: Developing, and managing predictive, causal models for smart cities must involve stakeholders with conflicting requirements, limited available data, limited knowledge and different ?city-subsystems? which interacts. Challenges can be summarized: (1) Present initiatives mostly focus on closed sets of topics leading to a narrow domain view. (2) Current simulations rely on small-scale, isolated models of real-world environments, where changes and migration of simulation results to real-world must be carried out manually. (3) Predictive causal models have to prove additional benefit by including smart cities ?behaviour? e.g. dynamic feedback loops of domains. Interdisciplinary, holistic approaches should integrate big static and dynamic data, the city emits from sources including IoT, documents or citizens. Data must be managed to provide the fundament for hybrid simulation models operated by multi-domain experts. This provides decision support for governance stakeholders, industry and citizens to influence the city. Close

Abstract: Social media are increasingly reflecting and influencing behavior of other complex systems. We investigate the relations between Twitter and stock market, in particular the Dow Jones Industrial Average contituents. In our previous work we adapted the well-known \event study" from economics to the analysis of Twitter data. We defined \events" as peaks of Twitter activity, and automatically classified sentiment in Twitter posts. During the Twitter peaks, we found significant dependence between the Twitter sentiment and stock returns: the sentiment polarity implies the direction of Cumulative Abnormal Returns Close

Abstract: Social interactions are composite, involve different communication layers and evolve in time. However, a rigorous analysis of the whole complexity of social networks has been hindered so far by lack of suitable data. Here we consider both the multi-layer and dynamic nature of social relations by analysing a diverse set of empirical temporal multiplex networks. We focus on the measurement and characterization of inter-layer correlations to investigate how activity in one layer affects social acts in another layer. We define observables able to detect when genuine correlations are present in empirical data, and single out spurious correlation induced by the bursty nature of human dynamics. We show that such temporal correlations do exist in social interactions where they act to depress the tendency to concentrate long stretches of activity on the same layer and imply some amount of potential predictability in the connection patterns between layers. Our work sets up a general framework to measure temporal correlations in multiplex networks, and we anticipate that it will be of interest to researchers in a broad array of fields. Close

Abstract: Most of complex physical systems are characterised by an emergent behaviour arising from the interaction of many particles dynamics. The resulting patterns at the macroscopic level can thus be linked to functional states of the system, which strongly depend on the topological features of the connections, on the single node intrinsic dynamics and environmental inputs. Nowadays these concepts are generalised and applied to a plethora of fields, including social dynamics, epidemics spreading, information flows and, in this particular case, also to the physiology of excitable biological media. In this perspective, we analysed emergent dynamics of the endocrine b-cells in the pancreas, as a typical example of biological electrically-coupled oscillators which release insulin in response to appropriate blood glucose levels. The primary focus was to establish a link between the underlying physical connectivity of the nodes and the functional state of the global network, modulated by specific operating conditions. A functional state was determined by looking at the robustness of the emergent electrical oscillations and the synchronisation patterns, investigated through a functional network approach. Indeed, a deep bond exists between the original physical network and the induced functional network. The possibile presence of multiplex via connections with other networks will also be discussed. Close