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This book focuses on applications of social network analysis in predictive policing. Data science is used to identify potential criminal activity by analyzing the relationships between offenders to fully understand criminal collaboration patterns. Co-offending networks—networks of offenders who have committed crimes together—have long been recognized by law enforcement and intelligence agencies as a major factor in the design of crime prevention and intervention strategies. Despite the importance of co-offending network analysis for public safety, computational methods for analyzing large-scale criminal networks are rather premature. This book extensively and systematically studies co-offending network analysis as effective tool for predictive policing. The formal representation of criminological concepts presented here allow computer scientists to think about algorithmic and computational solutions to problems long discussed in the criminology literature. For each of the studied problems, we start with well-founded concepts and theories in criminology, then propose a computational method and finally provide a thorough experimental evaluation, along with a discussion of the results. In this way, the reader will be able to study the complete process of solving real-world multidisciplinary problems.

Data mining. --- Police. --- Physics. --- Computer security. --- Data Mining and Knowledge Discovery. --- Policing. --- Applications of Graph Theory and Complex Networks. --- Systems and Data Security.

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This book focuses on applications of social network analysis in predictive policing. Data science is used to identify potential criminal activity by analyzing the relationships between offenders to fully understand criminal collaboration patterns. Co-offending networks—networks of offenders who have committed crimes together—have long been recognized by law enforcement and intelligence agencies as a major factor in the design of crime prevention and intervention strategies. Despite the importance of co-offending network analysis for public safety, computational methods for analyzing large-scale criminal networks are rather premature. This book extensively and systematically studies co-offending network analysis as effective tool for predictive policing. The formal representation of criminological concepts presented here allow computer scientists to think about algorithmic and computational solutions to problems long discussed in the criminology literature. For each of the studied problems, we start with well-founded concepts and theories in criminology, then propose a computational method and finally provide a thorough experimental evaluation, along with a discussion of the results. In this way, the reader will be able to study the complete process of solving real-world multidisciplinary problems.

Data mining. --- Police. --- Physics. --- Computer security. --- Data Mining and Knowledge Discovery. --- Policing. --- Applications of Graph Theory and Complex Networks. --- Systems and Data Security.

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This book presents a perspective of network analysis as a tool to find and quantify significant structures in the interaction patterns between different types of entities. Moreover, network analysis provides the basic means to relate these structures to properties of the entities. It has proven itself to be useful for the analysis of biological and social networks, but also for networks describing complex systems in economy, psychology, geography, and various other fields. Today, network analysis packages in the open-source platform R and other open-source software projects enable scientists from all fields to quickly apply network analytic methods to their data sets. Altogether, these applications offer such a wealth of network analytic methods that it can be overwhelming for someone just entering this field. This book provides a road map through this jungle of network analytic methods, offers advice on how to pick the best method for a given network analytic project, and how to avoid common pitfalls. It introduces the methods which are most often used to analyze complex networks, e.g., different global network measures, types of random graph models, centrality indices, and networks motifs. In addition to introducing these methods, the central focus is on network analysis literacy – the competence to decide when to use which of these methods for which type of question. Furthermore, the book intends to increase the reader's competence to read original literature on network analysis by providing a glossary and intensive translation of formal notation and mathematical symbols in everyday speech. Different aspects of network analysis literacy – understanding formal definitions, programming tasks, or the analysis of structural measures and their interpretation – are deepened in various exercises with provided solutions. This text is an excellent, if not the best starting point for all scientists who want to harness the power of network analysis for their field of expertise.

Application software. --- Physics. --- Computational complexity. --- Sociophysics. --- Econophysics. --- Data mining. --- Computer Appl. in Social and Behavioral Sciences. --- Applications of Graph Theory and Complex Networks. --- Complexity. --- Data-driven Science, Modeling and Theory Building. --- Data Mining and Knowledge Discovery.

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This book presents a perspective of network analysis as a tool to find and quantify significant structures in the interaction patterns between different types of entities. Moreover, network analysis provides the basic means to relate these structures to properties of the entities. It has proven itself to be useful for the analysis of biological and social networks, but also for networks describing complex systems in economy, psychology, geography, and various other fields. Today, network analysis packages in the open-source platform R and other open-source software projects enable scientists from all fields to quickly apply network analytic methods to their data sets. Altogether, these applications offer such a wealth of network analytic methods that it can be overwhelming for someone just entering this field. This book provides a road map through this jungle of network analytic methods, offers advice on how to pick the best method for a given network analytic project, and how to avoid common pitfalls. It introduces the methods which are most often used to analyze complex networks, e.g., different global network measures, types of random graph models, centrality indices, and networks motifs. In addition to introducing these methods, the central focus is on network analysis literacy – the competence to decide when to use which of these methods for which type of question. Furthermore, the book intends to increase the reader's competence to read original literature on network analysis by providing a glossary and intensive translation of formal notation and mathematical symbols in everyday speech. Different aspects of network analysis literacy – understanding formal definitions, programming tasks, or the analysis of structural measures and their interpretation – are deepened in various exercises with provided solutions. This text is an excellent, if not the best starting point for all scientists who want to harness the power of network analysis for their field of expertise.

Application software. --- Physics. --- Computational complexity. --- Sociophysics. --- Econophysics. --- Data mining. --- Computer Appl. in Social and Behavioral Sciences. --- Applications of Graph Theory and Complex Networks. --- Complexity. --- Data-driven Science, Modeling and Theory Building. --- Data Mining and Knowledge Discovery.

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This book contains the proceedings of the Sixth National Conference of the Italian Systems Society. The title, Towards a post-Bertalanffy Systemics, aims to underline the need for Systemics and Systems Science to generalize theoretically concepts related to complexity (the great enemy of Bertalanffy Systemics). Hopefully this goal should be achieved by working in an inter-disciplinary and trans-disciplinary fashion, using systemic concepts arising from various disciplines and from the original, or Bertalanffy Systemics, as well. The interdisciplinary nature of the original Systemics and its power of generalization were given, overall, by the fact that the problems and solutions of one discipline become problems and solutions for another. Today, the modeling and interpretation of multidisciplinary approaches and representations makes easier to recognize these interconnections. The context, however, has changed dramatically. Of course, the challenge is still to find theoretical generalizations and applications, even where we have a lot of specificities, but we know very little on how to combine them. We cannot, however, simply replace the old with the new, but we must introduce strategies to recognize, represent, model and act on new levels, combining multiple representations, functions and emergence. In many disciplines this has been already done, and inevitably well, since targets and projects are well specified and oriented. The challenge is to do it for Systemics, with the vocations of cultural and theoretical generalization. Examples of new issues introduced by such theoretical disciplinary improvements, dealt with by many disciplines, include the study of mesoscopic or middle-way level, of multiple and dynamic coherence, of equivalence/non-equivalence, of fractality, of networks, of non-causality, of non-invasiveness, of non-prescribability, of non-separability, of quasi properties, of symmetry properties, of topological dynamics, as well as of quantum theories and concepts. The conference was devoted to identifying, discussing and understanding possible interrelationships of theoretical disciplinary improvements, recognized as having prospective fundamental roles for a new post-Bertalanffy Systemics. The latter should be able to deal with problems related to complexity in a generalized way. In this context the inter-disciplinarity should consists, for instance, in a disciplinary reformulation of problems, as from algebraic to geometrical, from military to political, from biological to chemical, while the trans-disciplinarity should be related to the study of such reformulations and their properties.

Business. --- Operations research. --- Decision making. --- System theory. --- Physics. --- Business and Management. --- Operation Research/Decision Theory. --- Complex Systems. --- Complex Networks. --- System theory --- Network analysis (Planning) --- Mathematical models. --- Project networks --- Systems, Theory of --- Systems science --- Planning --- Science --- Philosophy --- Operations Research/Decision Theory. --- Applications of Graph Theory and Complex Networks. --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System analysis --- Natural philosophy --- Philosophy, Natural --- Physical sciences --- Dynamics --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Decision making