
IV. Ecosmomics: An Independent, UniVersal, Source CodeScript of Generative Complex Network Systems1. Network Physics: A Vital Interlinked Anatomy and Physiology Berner, Rico, et al. Adaptive Dynamical Networks. arXiv:2304.05652. This April entry by Humboldt University and University of Munich theorists including Thilo Gross and Jurgen Kurths can serve to gather and note current contributions which altogether presage for a robust 2023 synthesis of nature’s anatomical vitality. In the same while, see also On the Transient and Equilibrium Features of Growing Fractal Complex Networks by Alexandre Benatti and Luciano da Costa at arXiv:2034.12780, Dense Network Motifs Enhance Dynamical Stability by Bnaya Gross, et al (2304.12044) and Emergent Stability in Complex Network Dynamics by Chandrakala Meena, et al in Nature Physics (April 2023) among a growing number. Adaptive dynamical networks (ADNs) represent a broad class of systems that can change their connectivity over time depending on a dynamical state. Here we provide a detailed description of ADNs, note applications in research fields, highlight their arising dynamical phenomena, and give an overview of workable mathematical methods. (Excerpt) Biamonte, Jacob, et al. Complex Networks: From Classical to Quantum. arXiv:1702.08459. Biamonte, University of Malta, Mauro Faccin, Catholic University of Louvain, and Manlio De Domenico, Universitat Rovirai Virgili, Spain (search each) post a working “unified analysis” of nonlinear dynamic theories. As an integral result, a further confluence with “quantum Information science” is scoped out, which leads to a natural crossconvergence of these disparate fields. Recent progress in applying complex network theory to problems faced in quantum information and computation has resulted in a beneficial crossover between two fields. Complex network methods have successfully been used to characterize quantum walk and transport models, entangled communication networks, graph theoretic models of emergent spacetime and in detecting community structure in quantum systems. Information physics is setting the stage for a theory of complex and networked systems with quantum informationinspired methods appearing in complex network science, including informationtheoretic distance and correlation measures for network characterization. (Abstract excerpt) Bianconi, Ginestra. Interdisciplinary and Physics Challenges of Network Theory. arXiv:1509.00345. As a grand synthesis across nature and nurture proceeds apace, a Queen Mary University of London mathematical physicist sketches out how a newly found “universally of complex networks” can be extended even to quantum domains. Such a perception then reveals their generic, scalefree presence from every cosmic to cerebral realm. See also her paper Network Geometry from Complexity to Quantum Geometry (1511.04539), Complex Quantum Network Manifolds in Dimension d > 2 are ScaleFree with Christoph Rahmede (search, 1506.02648).and Emergent Complex Network Geometry (1412.3405). Bianconi, Ginestra. Multilayer Networks: Structure and Function. Oxford: Oxford University Press, 2018. A Queen Mary University of London mathematician provides a comprehensive tutorial on these novel insights into how ubiquitous and deep nature’s organic and cerebral connectivities actually are. After a technical survey, it covers Communities, Centrality Measures, Interdependence, Epidemic Diffusion, and much more. See also Multiplex Networks: Basic Formalism and Structural Properties by Cozzo, Emanuele, et al (SpringerBriefs, 2018). Multilayer networks is a rising topic in Network Science which characterizes the structure and the function of complex systems formed by several interacting networks. Multilayer networks research has been propelled forward by the wide realm of applications in social, biological and infrastructure networks and the large availability of network data, as well as by the significance of recent results, which have produced important advances. This book presents a comprehensive account of this emerging field by way of a theoretical and practical introduction to multilayer network science. Bianconi, Ginestra. Welcome to JPhys Complexity. Journal of Physics: Complexity. 1/010201, 2020. The Queen Mary University of London systems mathematician and author (search) introduces this inaugural Institute of Physics IOP journal. Actually its occasion is a bit overdue within the 21st century shift in physical studies from inorganic parts and energies to nature’s constant, intricate topologies and lively dynamics as they rise from statistical phenomena to genomic, physiology, neural and national phases. As the quote cites, once again this advance is about moving from separate pieces to their equally real interlinkages in a genesis uniVerse. Typical papers in the first two issues are Simplicial Complexes, Road Network Development, Classical Information Theory of Networks and Observables in Complex Quantum Networks. In addition, Guido Caldarelli offers A Perspective on Complexity and Networks Science, with an emphasis on financial phases. Boccaletti, Stefano, et al. Complex Dynamics in Networks, Multilayered Structures and Systems. Chaos. 26/6, 2016. An introduction to a focus section about dynamic network topologies which are actually composed of nested multitudes, often in selfsimilar communities, modules, rich hubs, and so on across every cosmic, physiological, cerebral, social media and ecosystem realm. For a sample pithy see A Biplex Approach to PageRank Centrality by Francisco Pedroche, et al, Optimal Distributions for Multiplex Logistic Networks by Luis Sola Conde, and Extracting Information from Multiplex Networks by Jacopo Iacovacci and Ginestra Bianconi. In the last years, network scientists have directed their interest to the multilayer character of realworld systems, and explicitly considered the structural and dynamical organization of graphs made of diverse layers between its constituents. Most complex systems include multiple subsystems and layers of connectivity and, in many cases, the interdependent components of systems interact through many different channels. Such a new perspective is indeed found to be the adequate representation for a wealth of features exhibited by networked systems in the real world. The contributions presented in this Focus Issue cover, from different points of view, the many achievements and still open questions in the field of multilayer networks, such as: new frameworks and structures to represent and analyze heterogeneous complex systems, different aspects related to synchronization and centrality of complex networks, interplay between layers, and applications to logistic, biological, social, and technological fields. (Boccaletti Abstract) Boccaletti, Stefano, et al. The Structure and Dynamics of Multilayer Networks. Physics Reports. Online July, 2014. The 150 page paper is also at arXiv:1407.0742. A tutorial by researchers from Complex Systems Institutes in Italy, Israel, UK, Spain, China, Singapore, and Portugal for these latest theories and discoveries by a “Worldwide Knowledge Society” as they array across nonlinear nature. As now commonly possible, a first part elucidates independent, universal network topologies and processes such as ensembles, resilience, percolation, synchronization, interactions, and so on. By this relational view extant living and social systems can be seen as pervaded with evolving neural networklike geometries and vital activities. Their evidence and effect is then noted for social, technical, economic, biomedicine, climatic, and psychological realms. By these understandings, palliative social and environmental improvements can be informed and accomplished. In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal or contextrelated properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of realworld systems, and explicitly considered the timevarying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamic. (Abstract) Boguna, Marian, et al. Network Geometry. arXiv:2001.03241. Six senior complexity scientists including Dmitri Krioukov and Shlomo Havlin offer a January 2020 posting which couldl be a bidecadal capsule of how much studies of nature’s innate node/link multiplex anatomy and physiology has been found in vivifying evidence from physical depths and galactic clusters and to evolutionary bodies, brains, groupings and onto economies and cultures. This entry describes how “fractal selfsimilarities, diffusion dynamics, and functional modularity” have been found from a chemicalspace renormalization to cellular communities across life’s biota, as shown in intricate displays. Into the 2010s, an increasing implication is the presence of an independent, mathematic source in exemplary manifestation at each and every scale and instance. See also Geometric Origins of SelfSimilarity in the Evolution of Real Networks by this group at 1912.00704 and Scalefree Networks Revealed from Finitesize Scaling at 1805.09512. Networks are natural geometric objects. Yet the discrete metric structure of shortest path lengths in a network is not the only reservoir of geometric distances. Other forms of networkrelated topologies are continuous latent spaces underlying many networks, and the effective geometry induced by dynamical processes. A growing amount of evidence shows that the three approaches are well related. Network geometry is thus quite efficient in discovering hidden symmetries, such as scaleinvariance, and other fundamental physical and mathematical properties, along with a variety of applications from the understanding how the brain works to routings in the Internet. Here, we review theoretical and practical developments in network geometry in the last two decades, and offer perspectives on future research for this novel complexity frontier. (Abstract)
Buchanan, Mark and Guido Caldarelli.
A Networked World.
Physics World.
February,
2010.
A lead article for a special report on how some two decades of complexity science have lately achieved both a theoretical depth and a worldwide utility. From immune systems and nonlinear genomes to H1N1 flu epidemics and banknote transfers, the same, distinctive principles are found everywhere as interactive agents constantly communicate. Other articles are Dirk Brockmann’s “Following the Money,” “Simplicity and Complexity” by Jim Crutchfield and Karoline Wiesner, and “The Flu Fighters” by Vittoria Colizza and Alessandro Vespignani. CaetanoAnolles, Gustavo, et al. Emergence of Hierarchical Modularity in Evolving Networks Uncovered by Phylogenomic Analysis. Evolutionary Bioinformatics. 15/1, 2019. University of Illinois, Heidelberg Institute for Theoretical Studies, Gebze Technical University, Turkey, Seoul National University, and European Molecular Biology Laboratory, Germany system biologists (search lead author) advance their phylogenomomic project by showing how this theoretical approach can provide novel explanations of life’s anatomic physiology. Networks describe how parts associate with each other to form integrated systems which often have modular and hierarchical structure. In biology, network growth involves two processes, one that unifies and the other that diversifies. Here, we propose a biphasic (bowtie) theory of module emergence. In the first phase, emerging, selforganized interactions join nodal parts into modular linkages. In the second phase, modular variants become components for a new generative cycle of higher level organization. Remarkably, phylogenomic analyses uncover this emergence in the rewiring of metabolomic and transcriptomeinformed metabolic networks, the nanosecond dynamics of proteins, and evolving metabolism, elementary functionomes, and protein domain networks. (Abstract) Caldarelli, Guido. ScaleFree Networks. Oxford: Oxford University Press, 2007. In some ten years since their discovery, this class of ‘real world,’ selforganized, interconnected systems distinguished by preferred nodes and links whose structure and dynamics repeat at any level have been studied both with regard to an independent universality, and to their manifest presence throughout in nature and society. As a result, a work such as this by the University of Rome complexity scientist can now describe their common properties, and as the quote notes, how an iterative invariance is evident from molecules to a metropolis. The structure of this book…starts with some basic notions of graph theory. After that we spend some time in explaining (mainly by use of fractals) why scalefree behavior is so interesting. After the theory we present an overview of the application of the above concepts. The areas selected are those of natural sciences (protein interactions, metabolic and gene regulatory networks, food webs, taxonomies, and river networks), information technology (Inter and WWW), and socioeconomic sciences (collaboration, cognitive networks, and financial systems). (4) Caldarelli, Guido, et al. Progress in the Physics of Complex Networks. European Physical Journal Special Topics. 212/1, 2012. Italian and British scientists introduce a topical issue on this growing interdisciplinary synthesis of statistical physics and nonlinear systems. A typical technical paper is “Von Neumann’s Growth Model: Statistical Mechanics and Biological Applications” by Andrea De Martino, Enzo Marinari, and Andrea Romualdi. At what point, by what means, might it then dawn and be availed that beyond all the theories, a profoundly phenomenal genesis uniVerse is being discovered? The presence of selfsimilar phenomena in Statistical Physics has a long tradition. From the classical studies of critical phenomena and renormalization group, we moved in the seventies and eighties of the last century to the analysis of scaleinvariance in the geometrical features of selfsimilar phenomena. A further evolution of this approach resulted in the analysis of scaleinvariance in the time dynamics of these systems with concepts such as SelfOrganized Criticality and studies of the socalled “sandpile models”, an example of which is present in this issue. Most of the activity is now focussed on the search for scaleinvariance in the topology, that is, in the way things are connected to each other. (1)
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