IV. Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

1. Network Physics: A Vital Interlinked Anatomy and Physiology

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 self-similar 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 multi-layer character of real-world 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 multi-layer 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)

Multilayer networks are formed by nodes connected by links describing interactions with different connotations. When the same set of nodes can be connected by different types of links, the resulting multilayer network is also called a multiplex network. Most social networks are multiplex, since the same set of people might be connected by different types of social ties or might communicate through different means of communication. As networks are ultimately a way to encode information about a complex set of interactions, one of the most pressing and challenging problems in network science is to extract relevant information from them. Here, we show evidence that the Multiplex PageRank algorithm and the recently introduced indicator function Θ˜S are able to extract from multiplex networks an information than cannot be inferred by analyzing the single layers taken in isolation or the aggregated network in which links of different type are not distinguished. (Iacovacci 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 network-like 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 context-related 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 real-world systems, and explicitly considered the time-varying 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)

If we just turn our eyes to the immense majority of phenomena that occur around us, we realize immediately that they are nothing but the result of the emergent dynamical organization of systems that, on their turn, involve a multitude of basic constituents (or entities) interacting with each other via somehow complicated patterns. One of the major effort of modern physics is then providing proper and suitable representations of these systems, where constituents are considered as nodes (or units) of a network, and interactions are modeled by links of that same network. Indeed, having such a representation in one hand and the arsenal of mathematical tools for extracting information in the other (as inherited by several gifted centuries of thoughts, concepts and activities in applied mathematics and statistical mechanics) is the only suitable way through which we can even dare to understand the observed phenomena, identify the rules and mechanisms that are lying behind them, and possibly control and manipulate them conveniently. (4)

The last fifteen years have seen the birth of a movement in science, nowadays very well known under the name of complex networks theory. It involved the interdisciplinary effort of some of our best scientists in the aim of exploiting the current availability of big data in order to extract the ultimate and optimal representation of the underlying complex systems and mechanisms. The main goals were i) the extraction of unifying principles that could encompass and describe (under some generic and universal rules) the structural accommodation that is being detected ubiquitously, and ii) the modeling of the resulting emergent dynamics to explain what we actually see and experience from the observation of such systems. (4)

Complexity science is the study of systems with many interdependent components, which, in turn, may interact through many different channels. Such systems – and the self-organization and emergent phenomena they manifest – lie at the heart of many challenges of global importance for the future of the Worldwide Knowledge Society. The development of this science is providing radical new ways of understanding many different mechanisms and processes from physical, social, engineering, information and biological sciences. (6-7)

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 self-similarities, diffusion dynamics, and functional modularity” have been found from a chemical-space 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 Self-Similarity in the Evolution of Real Networks by this group at 1912.00704 and Scale-free Networks Revealed from Finite-size 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 network-related 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 scale-invariance, 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.

To reflect, in addition to all the parts and pieces of science’s reductive phase, such organizational dynamics ought to be seen as equally real, which can then distinguish and reveal a wholly new kind of nature. Two attributes are of especial note. From galaxies to Gaia, a grand nested repetition occurs as self-similar patterns and processes iterate over and over. And this epochal discovery, not possible much earlier, can imply that such universal phenomena springs from and exemplify original, independent physical and mathematical principles, as if a cosmic genetic code.

Caetano-Anolles, 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 (bow-tie) theory of module emergence. In the first phase, emerging, self-organized 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 transcriptome-informed metabolic networks, the nanosecond dynamics of proteins, and evolving metabolism, elementary functionomes, and protein domain networks. (Abstract)

Cajic, Pavle, et al. On the information-theoretic 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 information-theoretic formalism developed allows new and more subtle connection patterns in complex networks to be studied.

Caldarelli, Guido. Scale-Free Networks. Oxford: Oxford University Press, 2007. In some ten years since their discovery, this class of ‘real world,’ self-organized, 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 scale-free 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 socio-economic 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 self-similar 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 scale-invariance in the geometrical features of self-similar phenomena. A further evolution of this approach resulted in the analysis of scale-invariance in the time dynamics of these systems with concepts such as Self-Organized Criticality and studies of the so-called “sandpile models”, an example of which is present in this issue. Most of the activity is now focussed on the search for scale-invariance 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 Multi-level 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 self-organization, 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 “humanity-organism.” 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 auto-catalytic process networks, organized in a multi-level 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)

Humanity, by becoming an ‘organism’, is becoming de facto mortal. Cultural evolution as we used to think of it, is over. It will become more similar to a process of adaptation and learning at the level of humanity that can lead to its disappearance at any time. The new humanity – organism is alone on its evolutionary path and we can ask ourselves if we can afford to have it guided by random trials and errors. The kinds of collective cognition and behaviours that humanity will adopt in this new phase of its existence will determine its chances of survival in the future. (9)

David Chavalarias is the director of the Complex Systems Institute of Paris and Vice-President of the Complex Systems Society. He holds a PhD from the Ecole Polytechnique in cognitive sciences and attended the Ecole Normale Supérieure de Cachan in Mathematics and Computer Sciences. Currently permanent researcher at the National Center for Scientific Research (CNRS) in France, he studies the social and cognitive dynamics, both. His interdisciplinary research includes: quantitative epistemology, information visualization, modelling of the cultural dynamics, socio-semantic networks modelling, scientific discovery processes and cognitive economics.

Cimini, Giulio, et al. The Statistical Physics of Real-World 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 Self-Learning 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 self-organization, scale invariance, mixed distributions and ensemble non-equivalence, 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 real-world networks. We further survey the statistical physics frameworks that reproduce more complex, semi-local 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 many-body interaction between the nodes. In this case the faces of the simplicial complex are naturally described by the Bose-Einstein, Boltzmann and Fermi-Dirac 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 Bose-Einstein or the Fermi-Dirac distribution only. (Abstract flavor)

Previous   1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10  Next  [More Pages]