
IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic CodeScript Source1. Network Physics: A Vital Interlinked Anatomy and Physiology
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) Cajic, Pavle, et al. On the informationtheoretic formulation of network participation. arXiv:2307.12556. University of Sydney systems theorists including Joseph Lizier describe an improved finesse of a method by which to parse relative multiples meanings. The participation coefficient is a measure of the diversity of a node's connections with respect to a modular partition. While diversity metrics have been studied in other fields such as ecology, they have not been applied to networks. Here we show that the distinction is an approximation to participation entropy and use the additive properties of entropy to develop new metrics of connection diversity. Our informationtheoretic formalism developed allows new and more subtle connection patterns in complex networks to be studied. 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) Carletti, Timoteo, et al. Global Topological Synchronization on Simplical and Cell Complexes. arXiv:2208.14783. TC, Lorenzo Giambagli, University of Namur, Belgium and Ginestra Bianconi, Queen Mary University, London theorists press their studies of network phenomena, which as an ecosmic anatomy and physiology seems ever graced by such innate facilities. See also Solitary States in Complex Networks: Impact of Topology by Anna Zakharova, et al (2208.14911). Chavalarias, David. From Inert Matter to the Global Society: Life as Multilevel Networks of Processes. Philosophical Transactions of the Royal Society B. February, 2020. In this Unifying the Essential Concepts of Biological Networks issue, a Parisian cognitive scientist (bio below) illumes a cosmic to congress synthesis due to the generative activity of selforganization, autopoiesis, biocatalysis, recurrent scales, and more. This view leads a “triple closure” (see Abstract) made up of member components, active relations, and an integral unity. Life’s evolutionary basis and intent is then seen as a constant fulfillment of this iconic, triune whole. A consequentl rise of cerebral cognition, collective intelligence, and cultural learning can also be observed. As this universe to human course proceeds, our global phase is seen to be emerging into a “humanityorganism.” In closing, it is noted that this worldwide advance must not be left to chance, rather a common, informed, popular, concerted effort is imperative to bring to fruition. A billion years have passed since the first life forms appeared. Since then, life has continued to form complex associations between emergent levels of interconnection. Advances in molecular chemistry and theoretical biology based on a systems view can now conceptualize life’s origins and complexity from three notions of closure: processes, autocatalysis and constraints. This integral paradigm can then trace the physical levels of the organization of matter from physics to biology and society without resorting to reductionism. The phenomenon of life thus becomes a contingent complexification until life emerges as a network of autocatalytic process networks, organized in a multilevel manner. A living systems approach inevitably reflects on cognition; and on the deep changes that affects humanity by way of our cultural evolution. (Abstract Excerpt) Cimini, Giulio, et al. The Statistical Physics of RealWorld Networks. arXiv:1810.05095. In a paper to appear in the new Nature Reviews Physics (2019), IMT School for Advanced Studies, Lucca, Italy researchers including Guido Caldarelli expand appreciations of nature’s universal complex nodal and relational networks. A widely separate yet integral rooting of our global civilization into physical condensed matter can then be achieved. An illustration displays how the same multiplex phenomena arises from a independent source which is exemplified from agriculture and industry to travel and trade. See also The Dynamics of Knowledge Acquisition via SelfLearning in Complex Networks by this team at 1802.09337. Statistical physics is the natural framework to model complex networks. In the last twenty years, it has brought novel physical insights on a variety of emergent phenomena, such as selforganization, scale invariance, mixed distributions and ensemble nonequivalence, which cannot be deduced from individual constituents, along with information theory and the principle of maximum entropy. We review the statistical physics approach for complex networks and the null models for the various physical problems, focusing on the analytic frameworks reproducing the local features of the network. We show how these models have been used to detect statistically significant and predictive structural patterns in realworld networks. We further survey the statistical physics frameworks that reproduce more complex, semilocal network features using Markov chain Monte Carlo sampling, and the models of generalised network structures such as multiplex networks, interacting networks and simplicial complexes. (Abstract edits) Cinardi, Nicola, et al. Quantum Statistics in Network Geometry with Fractional Flavor. arXiv:1902.10035. Systems physicists NC and Andrea Rapisarda, University of Catania, and Ginestra Bianconi, Queen Mary University of London continue to finely season nature’s true anatomy and physiology as it ever arrays in similar kinds from quantum to neuronal realms. Growing network models have been shown to display emergent quantum statistics when nodes are associated to a fitness value describing the intrinsic ability of a node to acquire new links. Recently it has been shown that quantum statistics emerge also in a growing simplicial complex model called Network Geometry with Flavor which allow for the description of manybody interaction between the nodes. In this case the faces of the simplicial complex are naturally described by the BoseEinstein, Boltzmann and FermiDirac distribution depending on their dimension. We show that in this case the statistical properties of the faces of the simplicial complex are described by the BoseEinstein or the FermiDirac distribution only. (Abstract flavor) Cohen, Reuven and Shlomo Havlin. Complex Networks: Structure, Robustness and Function.. Cambridge: Cambridge University Press;, 2010. A BarIlan University, Israel mathematician and a physicist provide a comprehensive survey of ever growing realizations of an innately interconnected nature, organisms, and societies. Examining important results and analytical techniques, this graduatelevel textbook is a stepbystep presentation of the structure and function of complex networks. Using a range of examples, from the stability of the internet to efficient methods of immunizing populations, and from epidemic spreading to how one might efficiently search for individuals, this textbook explains the theoretical methods that can be used, and the experimental and analytical results obtained in the study and research of complex networks. Giving detailed derivations of many results in complex networks theory, this is an ideal text to be used by graduate students entering the field. Costa, Luciano da Fontoura, et al. Analyzing and Modeling RealWorld Phenomena with Complex Networks. Advances in Physics. 60/3, 2011. Drawing upon a departmental focus on this field, eight University of Sao Paulo physicists provide a 108 page survey, with 565 references, of this real dynamic materiality across nature and society. After noting Basic Concepts, topical areas are Social, Communications, Economy, Finance, Computers, Internet, World Wide Web, Citations, Transportation Power Grids, Biomolecular, Medicine, Ecology, Neuroscience, Linguistics, Earthquakes, Physics, Chemistry, Mathematics, Climate, and Epidemics – that is everywhere. From these many exemplars can be distilled a common, independent, complex system topology. Circa 2012, how could it dawn upon international collaborative science that this ubiquitous discovery is actually revealing a procreative genesis universe? In such regard, other such citations lately weigh in, e.g., Li and Peng in Complex Human Societies, Dorogovtsev, the Nature Physics Insight review, all herein, boding a critical credence. We append extended quotes. Drawing upon a departmental focus on this field, eight University of Sao Paulo physicists provide a 108 page survey, with 565 references, of this real dynamic materiality across nature and society. After noting Basic Concepts, topical areas are Social, Communications, Economy, Finance, Computers, Internet, World Wide Web, Citations, Transportation Power Grids, Biomolecular, Medicine, Ecology, Neuroscience, Linguistics, Earthquakes, Physics, Chemistry, Mathematics, Climate, and Epidemics – that is everywhere. From these many exemplars can be distilled a common, independent, complex system topology. Circa 2012, how could it dawn upon international collaborative science that this ubiquitous discovery is actually revealing a procreative genesis universe? In such regard, other such citations lately weigh in, e.g., Li and Peng in Complex Human Societies, Dorogovtsev, the Nature Physics Insight review, all herein, boding a critical credence. We append extended quotes. Courtney, Owen and Ginestra Bianconi. Generalized Network Structures: The Configuration Model and the Canonical Ensemble of Simplicial Complexes. arXiv:1602.04110. Queen Mary University, London mathematicians employ this topological phrase for their perception of connective patterns commonly found across networks. After technicalities, generic, repetitive webs are defined which form and flow everywhere. See also such later postings as Centralities of Nodes and Influences of Layers in Large Multiplex Networks at 1703.05833, Weighted Growing Simplicial Complexes. 1703.01187, and Emergent Hyperbolic Network Geometry in Nature Scientific Reports (7/41974, 2017) which each have GB as a coauthor. Altogether these distinct, recurrent features would seem to imply and arise from a natural propensity. Simplicial complexes are generalized network structures able to encode interactions occurring between more than two nodes. Simplicial complexes describe a large variety of complex interacting systems ranging from brain networks, to social and collaboration networks. Here we characterize the structure of simplicial complexes using their generalized degrees that capture fundamental properties of one, two, three or more linked nodes. We evaluate the entropy of these ensembles, finding the asymptotic expression for the number of simplicial complexes in the configuration model. We provide the algorithms for the construction of simplicial complexes belonging to the configuration model and the canonical ensemble of simplicial complexes. (Abstract)
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