Reconstructing networks

Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, we shall focus on the inference methods rooted in statistical physics and information theory. The discussion will be organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections.Comment: 107 pages, 25 figure

Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis

Interbank markets are often characterised in terms of a core-periphery network structure, with a highly interconnected core of banks holding the market together, and a periphery of banks connected mostly to the core but not internally. This paradigm has recently been challenged for short time scales, where interbank markets seem better characterised by a bipartite structure with more core-periphery connections than inside the core. Using a novel core-periphery detection method on the eMID interbank market, we enrich this picture by showing that the network is actually characterised by multiple core-periphery pairs. Moreover, a transition from core-periphery to bipartite structures occurs by shortening the temporal scale of data aggregation. We further show how the global financial crisis transformed the market, in terms of composition, multiplicity and internal organisation of core-periphery pairs. By unveiling such a fine-grained organisation and transformation of the interbank market, our method can find important applications in the understanding of how distress can propagate over financial networks.Comment: 17 pages, 9 figures, 1 tabl

Reconstructing networks

Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, the authors focus on the inference methods rooted in statistical physics and information theory. The discussion is organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections

Optimizing complex networks models

Analyzing real-world networks ultimately amounts at com- paring their empirical properties with the outcome of a proper, statistical model. The far most common, and most useful, approach to define benchmarks rests upon the so-called canonical formalism of statistical mechanics which has led to the definition of the broad class of models known as Exponential Random Graphs (ERGs). Generally speaking, employing a model of this family boils down at maximizing a likelihood function that embodies the available information about a certain system, hence constituting the desired benchmark. Although powerful, the aforementioned models cannot be solved analytically, whence the need to rest upon numerical recipes for their optimization. Generally speaking, this is a hard task, since real-world networks can be enormous in size (for example, consisting of billions of nodes and links), hence requiring models with ‘many’ parameters (say, of the same order of magnitude of the number of nodes). This evidence calls for optimization algorithms which are both fast and scalable: the collection of works constituting the present thesis represents an attempt to fill this gap. Chapter 1 provides a quick introduction to the topic. Chapter 2 deals specifically with ERGs: after reviewing the basic concepts constituting the pillars upon which such a framework is based, we will discuss several instances of it and three different numerical techniques for their optimization. Chapter 3, instead, focuses on the detection of mesoscale structures and, in particular, on the formalism based upon surprise: as the latter allows any partition of nodes to be assigned a p-value, detecting a specific, mesoscale structural organization can be understood as the problem of finding the corresponding, most significant partition - i.e. an optimization problem whose score function is, precisely, surprise. Finally, chapter 4 deals with the application of a couple of ERGs and of the surprise-based formalism to cryptocurrencies (specifically, Bitcoin)

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well

Перспективи розвитку професійно спрямованих мовних компетенцій в сучасній науці

У збірнику представлені матеріали Міжнародної науково-практичної конференції для студентів немовних спеціальностей, що висвітлюють питання філології, психології, педагогіки, історії, екології, інформатики, менеджменту, економіки та медицини