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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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IV. Ecosmomics: An Independent, UniVersal, Source Code-Script of Generative Complex Network Systems

1. Network Physics: A Vital Interlinked Anatomy and Physiology

Kostic, Daniel, et al. Unifying the Essential Concepts of Biological Networks. Philosophical Transactions of the Royal Society B. February, 2020. DR, University of Bordeaux, Claus Hilgetaf, University Medical Center Hamburg, and Marc Tittgemeyer, MPI Metabolism Research introduce a special issue with this integrative title. Its content is composed of both life science and philosophical considerations since both views need join together. For example, see General Theory of Topological Explanations and Explanatory Asymmetry by D. Kostic, Hierarchy and Levels by William Bechtel, Exploring Modularity by Maria Serban, and Network Architectures Supporting Learnability by Perry Zurn and Danielle Bassett, From Inert Matter to Global Society by David Chavalarias and Evolving Complexity by Richard Sole and Sergi Valverde (search for these last three).

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organizational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels and network hierarchies. (Abstract)

Our organism constantly integrates information about the internal state with external environmental cues to adapt behavioural and autonomic responses to ensure physiological homeostasis. The Translational Neurocircuitry Group investigates how the human brain represents, integrates and prioritizes these internal and external signals to initiate adequate behavioural and physiological responses with a special focus on circuit-level models, metabolic mechanisms and human cognition. (Marc Tittgemeyer)

Kovacs, Istvan, et al. Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics. PLoS One. 5/9, 2010. Cited more in Common Code, in this 100 page entry with bioinformatic programs and references, Semmelweis University, Budapest, living system scientists, including Peter Csermely, parse modular networks to uncover a ubiquitous topological feature. Indeed, nature seems intent on forming communal groupings of an appropriate size and populace at each and every strata and instance. Might one even broach an “ubuntu Universe.”

Krioukov, Dmitri, et al. Network Cosmology. Nature Scientific Reports. 2/793, 2012. . On occasion, a paper comes along of such unique, meritous content that it bodes for a significant breakthrough and synthesis. A team of five University of California, San Diego, systems scientists with Marian Boguna, a University of Barcelona physicist, proceed via sophisticated quantifications to discern the same nonlinear dynamics that infuse from proteins to cities within celestial topological networks. Its technical acumen and depth requires several excerpts. For example, Figure 2, “Mapping between the de Sitter universe and complex networks” illustrates many isomorphic affinities. As per Figure 4, “Degree distribution and clustering in complex networks and space time,” Internet, social network, brain anatomy, and hyperbolic spatial lineaments all graph on the same line, indicating common node and link geometries. As the quotes allude, a grand unification of universe, life, cognition, and humankind could be in the offing, a nascent witness of a biological genesis uniVerse.

Kumpula, Jussi, et al. Emergence of Communities in Weighted Networks. Physics Review Letters. 99/228701, 2007. As scale-free networks grow in intricacy, they reveal an inherent propensity to form modular and communal topologies. This “quite general paradigm” is then evident across a nested nature from metabolic to neural to societal systems, each amenable to this common physical explanation. And one might add, what is implied by such findings is an organic developing cosmos.

Network theory has undergone a remarkable development over the last decade and has contributed significantly to our understanding of complex systems, ranging from genetic transcriptions to the Internet and human societies. (228701-1) Understanding how the microscopic mechanisms translate into mesoscopic communities and macroscopic social systems is a key problem in its own right and one that is accessible within the scope of statistical physics. (228701-1)

Laurent, Hebert-Dufresne, et al. Complex Networks as an Emerging Property of Hierarchical Preferential Attachment. Physical Review E. 92/6, 2015. Cited also in Universality Affirmations, University of Laval, Quebec and University of Barcelona physicists open this survey on the state of complexity science by tracing its advent to a 1962 paper The Architecture of Complexity by the pioneer theorist Herbert Simon in the Proceedings of the American Philosophical Society (106/467). Some half century later, as this 2015 section documents, the Grail goal of one, same, infinitely iterated, self-organizing system has been proven from quantum to human to cosmic realms, so as to imply a common, independent, universally manifest, source.

Laurienti, Paul, et al. Universal Fractal Scaling of Self-Organized Networks. Physica A. 390/20, 2011. Cited more in Common Code, after some two decades of complex systems studies from every angle, in disparate fields and terms, on every continent, a maturity is lately being reached so it is possible, for example, for this team of Wake Forest University biomedical researchers to propose a natural “universality” of “node and interaction” dynamic network phenomena. To wit, the same fractal pattern and process faithfully recurs across broad Biological, Information, Social, and Technological domains. These extended quotes might portend, circa 2011, a new animate nature suffused with intrinsic creativities that repeat and reiterate across every regnant realm. At what point, and by what insight, might this realization become a revolution, and its spontaneity be appreciated as genetic in kind?

Lee, Kyu-Min, et al. Towards Real-World Complexity: An Introduction to Multiplex Networks. European Physical Journal B. 88/2, 2015. In this edition for Condensed Matter and Complex Systems, Korea University physicists offer a succinct tutorial for these novel findings of a lively nature from cosmos to cerebral to culture as graced by nested networks in iterative hierarchies. With statistical and nonlinear physics as Keywords, the paper joins a mid 2010s revolution from mechanical particles only to pervasive animate, neural interconnections, whose latest epitome of human persons and societies is able to achieve its own vital self-realization.

Many real-world complex systems are best modeled by multiplex networks of interacting network layers. The multiplex network study is one of the newest and hottest themes in the statistical physics of complex networks. Pioneering studies have proven that the multiplexity has broad impact on the system’s structure and function. In this Colloquium paper, we present an organized review of the growing body of current literature on multiplex networks by categorizing existing studies broadly according to the type of layer coupling in the problem. Major recent advances in the field are surveyed and some outstanding open challenges and future perspectives will be proposed. (Abstract)

Lee, Sang Hoon. Network Nested as Generalized Core-Periphery Structures. arXiv:1602.00093. We cite this entry by a Korea Institute for Advanced Study physicist as an example of how such a generic recurrent scale, which seeks and reaches complementary fast dense and slower expanse phases, are gaining notice as natural archetypes from universe to human. See also later entries on this eprint site by the author and colleagues for practical applications such as 3D chromosome and power-grid geometries.

The concept of nestedness, in particular for ecological and economical networks, has been introduced as a structural characteristic of real interacting systems. We suggest that the nestedness is in fact another way to express a mesoscale network property called the core-periphery structure. With real ecological mutualistic networks and synthetic model networks, we define the network-level measures for nestedness and core-periphery-ness in the case of weighted and bipartite networks. Therefore, there must exist structurally interwoven properties in more fundamental levels of network formation, behind this seemingly obvious relation between nestedness and core-periphery structures. (Abstract)

Lee, UnCheol and George Mashour. Role of Network Science in the Study of Anesthetic State Transitions. Anesthesiology. 129/1029, 2018. University of Michigan Medical School neuroscientists, who are involved with consciousness studies at UMMS (search each author), illustrate how the common multiplex networks found everywhere also provide functional structures as brains pass into and out of relatively unconscious states. It is then affirmed that as these conditions exhibit a self-organized criticality with scale-free power laws, this phenomena appears to manifestly arise from universal, lawful principles. Within this approach, the array of nodes, links, hubs, and dynamic topologies are cited as a major determinant of global information processing.

University of Michigan Medical School neuroscientists, who are involved with consciousness studies at UMMS (search each author), illustrate how the common multiplex networks found everywhere also provide functional structures as brains pass into and out of relatively unconscious states. It is then affirmed that as these conditions exhibit a self-organized criticality with scale-free power laws, this phenomena appears to manifestly arise from universal, lawful principles. Within this approach, the array of nodes, links, hubs, and dynamic topologies are cited as a major determinant of global information processing.

Leli, Vito, et al. Deep Learning Super-Diffusion in Multiplex Networks. arXiv:1811.04104. As the Abstract details, VL, Saeed Osat and Timur Tlyachev, Skolkovo Institute of Science and Technology, Moscow and Jacob Biamonte, Deep Quantum AI, Moscow conceive a working method based on natural phenomena so to better analyze and design intricate nets of many kinds.

Complex network theory has shown success in understanding the emergent and collective behavior of complex systems. Many real-world complex systems were recently discovered to be more accurately modeled as multiplex networks in which each interaction type is mapped to its own network layer such as transportation networks, coupled social networks, metabolic and regulatory networks, etc. A salient physical phenomena emerging from multiplexity is super-diffusion via an accelerated diffusion by the multi-layer structure as compared to any single layer. Here we show that modern machine (deep) learning, such as fully connected and convolutional neural networks, can classify and predict the presence of super-diffusion in multiplex networks. (Abstract excerpts)

Li, Aming, et al. Evolution of Cooperation on Temporal Networks. Nature Communications. 11/2259, 2020. By a novel application of network science to social activities, an eight person team from Peking University, Northeastern University, Harvard Medical School and Princeton University (Simon Levin) illuminates a deeper natural basis for beneficial behaviors to both members and groups. As the quotes says, these heretofore unknown features can aid better explanations and usage.

Population structure is a key determinant in fostering cooperation among naturally self-interested individuals in microbial populations, social insect groups, and human societies. Prior research has focused on static structures, and yet most interactions are changing in time and form a temporal network. Surprisingly, we find that network temporality actually enhances the evolution of cooperation relative to comparable static networks, despite the fact that bursty interaction patterns generally impede. We resolve this tension by a measure which quantifies the amount of temporality in a network, so to reveal an intermediate level that boosts cooperation. (Abstract excerpt)

Explaining the evolution of durable, widespread cooperative behaviour in groups of self-interested individuals has been a challenge since the time of Darwin. In response, researchers have turned to the critical role played by the underlying interaction networks, in which nodes represent individuals and links represent interactions. It has been shown that the nontrivial population structures represented by both homogeneous and heterogeneous networks permit the formation of stable clusters of cooperators (altruists), with higher individual payoffs while also resisting defectors (egoists). As such, both theoretical analysis and behavioural experiments point to network structure as a key ingredient for the emergence of cooperation. (2)

Li, Angsheng, et al. Discovering Natural Communities in Networks. Physica A. Online May, 2015. We note this paper amongst many to exemplify a robust maturity of complexity system science. Chinese Academy of Sciences, Beijing, theorists treat these pervasive relational formations as an independent, universal phenomena from which generic principles and properties can be identified. The upshot would be to wonder where does this dynamic mathematical geometry come from, what kind of a universe to human self-realizing procreation?

Natural or true communities are basic to many interacting systems in nature, society and networks. Identifying and analyzing natural communities of real world networks are essential to understanding the networks, with potential applications in understanding, for instance: the roles and functions of the modules of social and technological systems, the roles and mechanisms of social groups in nature and society, the mechanisms of group intelligence, the mechanisms of interacting learning and games among social groups, diagnosing and curing of complex diseases, and designing of new medicines etc. Our algorithm provides for the first time a method which may exactly identify or precisely approximate the natural or true communities of many real world networks and interacting system in nature and society. (2)

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