Network based data oriented methods for application driven problems

Networks are amazing. If you think about it, some of them can be found in almost every single aspect of our life from sociological, financial and biological processes to the human body. Even considering entities that are not necessarily connected to each other in a natural sense, can be connected based on real life properties, creating a whole new aspect to express knowledge. A network as a structure implies not only interesting and complex mathematical questions, but the possibility to extract hidden and additional information from real life data. The data that is one of the most valuable resources of this century. The different activities of the society and the underlying processes produces a huge amount of data, which can be available for us due to the technological knowledge and tools we have nowadays. Nevertheless, the data without the contained knowledge does not represent value, thus the main focus in the last decade is to generate or extract information and knowledge from the data. Consequently, data analytics and science, as well as data-driven methodologies have become leading research fields both in scientific and industrial areas. In this dissertation, the author introduces efficient algorithms to solve application oriented optimization and data analysis tasks built on network science based models. The main idea is to connect these problems along graph based approaches, from virus modelling on an existing system through understanding the spreading mechanism of an infection/influence and maximize or minimize the effect, to financial applications, such as fraud detection or cost optimization in a case of employee rostering

Agent-based models for assessing the risk of default propagation in interconnected sectorial financial networks

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2019, Tutor: Jordi Nin[en] Financial Institutions perform risk assessments continuously in order to judge if certain companies are viable and should receive funding or loans to prevent companies to go bankrupt (default). This task helps keeping the financial system healthy. However, risk assessment is a tremendously difficult task since there are many variables to take into account. This work is a continuation of Barja et al., 2019, in which a model is posed to simulate customer-supplier relationships. The model helps to explore the risk of default of companies under certain circumstances. We extended the model in several ways to make it more realistic. The main objective of the work is to gain better insights in how defaulted companies affect non-defaulted ones. This is analyzed by k eeping track of the possible default cascades produced when a company goes bankrupt and stops paying. In addition, studying how financial networks behave, it is also possible shed some light about how the risk of specific companies or economical sectors can be tracked

Multilayer Networks

In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio

Actuarial and financial risk management in networks

Interconnectedness constitutes a key characteristic of actuarial and financial systems. In regular times, it facilitates the provision of the systems’ important services to society. In times of crisis, however, it enables the spread of contagious distress that may adversely affect the overall economy and amplify crisis situations. In this thesis, we introduce and analyze two financial and one actuarial network model representing three particular risk management problems that arise from different forms of interconnectedness. First, we consider the spread of financial losses and defaults in a comprehensive model of a banking network. Distress therein may propagate through various forms of connections such as direct financial obligations, bankruptcy costs, fire sales, and cross-holdings. For the integrated financial market, we prove the existence of a price-payment equilibrium and design an algorithm for its computation. The corresponding number of defaults is analyzed in several comparative case studies. These illustrate the individual and joint impact of the considered interaction channels on systemic risk. Second, we study the problem of minimizing market inefficiencies, defined as deviations of realized asset prices from fundamental values, as a function of the network of banks’ overlapping asset portfolios. Prices are pressured from trading actions of the leverage targeting banks, which rebalance their portfolios in response to exogenous asset shocks. We prove the existence of a network of efficient asset holdings and characterize its properties and sensitivities. In particular, we find that the standard paradigm of asset portfolio diversification may cause tremendous market inefficiencies, especially during crisis situations. Third, we consider insurance against cyber epidemics. Infectious cyber threats, such as viruses and worms, diffuse within a network of possibly insured parties. Since the infection may affect many different agents at the same time, a provider of cyber insurance is exposed to significant accumulation risk. We build and analyze a stochastic model of losses generated by infectious cyber threats based on interacting particle systems and marked point processes. Together with a novel polynomial and mean-field approximation, our approach allows to explicitly compute prices for different forms of cyber insurance contracts. Numerical case studies demonstrate the impact of the network topology and indicate that higher order approximations are indispensable for the analysis of non-linear contracts

Integrity Verification for SCADA Devices Using Bloom Filters and Deep Packet Inspection

In the past, SCADA networks were made secure through undocumented, proprietary protocols and isolation from other networks. Today, modern information technology (IT) solutions have provided a means to enhance remote access through use of the Internet. Unfortunately, opening SCADA networks to the Internet has provided routes of attack. Cyber attacks on these networks are becoming more common and can inflict considerable damage to critical infrastructure systems. Furthermore, devices on these networks can be infected with malware that causes them to falsify their responses to operators, concealing alternate operation or hiding alarm conditions. Considering their applications, securing these networks translates to improved physical security in the real world. Since modern IT solutions are impractical to deploy in the resource constrained SCADA networks, other solutions must be researched. This research evaluates an integrity verification system implemented on a Xilinx ML507 development board called the SIEVE system. The design incorporates Bloom filters and SCADA-specific intrusion detection techniques to speed identification of invalid commands and current sensing to investigate whether or not a device correctly carried out a given command. Results show that the SIEVE system is able to inspect and correctly identify 100% of network traffic at a 200 command per second frequency. Correct identification of valid MODBUS/TCP traffic begins to fail at 350 commands per second, introducing false positives. Tests of the Bloom filters show that they reduce the time necessary to process and log invalid MODBUS/TCP commands by 4.5% to 2328.06% depending on the number of operations performed by the command