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

1. Network Physics: A Vital Anatomy and Physiology

Demongeot, Jacques, et al. Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks. Entropy. 20/1, 2018. Six theorists from France, Chile, and Tunisia contend that although nature’s ubiquitous geometries may vary in kind across these disparate domains, at this later date in their extensive study, each instance seems to exemplify the same generic form and process. In their regard, this similarity may be expressed in some degree by relative entropic levels.

Networks used in biological applications at different scales (molecule, cell and population) are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions as well as in their discrete Boolean versions (e.g., non-linear Hopfield system). We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i) attractor entropy, (ii) isochronal entropy and (iii) entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment. (Abstract edits)

Diego, Xavier, et al. Key Features of Turing Systems are Determined Purely by Network Topology. Physical Review X. 8/021071, 2018. Into the 21st century, systems scholars with postings in Barcelona and Tubingen including James Sharpe advise that Alan Turing’s 1930s and 1940s theories and writings on pattern and morphogenesis formations by way of a computational source can now be appreciated to involve this heretofore unrecognized feature of generative topological linkages.

Into the 21st century, systems scholars with postings in Barcelona and Tubingen including James Sharpe advise that Alan Turing’s 1930s and 1940s theories and writings on pattern and morphogenesis formations by way of a computational source can now be appreciated to involve this heretofore unrecognized feature of generative topological linkages.

Dong, Gaogao, et al. Resilience of Networks with Community Structure Behaves as if Under an External Field. Proceedings of the National Academy of Sciences. 115/6911, 2018. A team of nine Chinese, Israeli, and American complexity theorists including Shlomo Havlin and Eugene Stanley expand understandings of nature’s ubiquitous social physiology by adding deeper rootings in and affinities with condensed matter physical phenomena.

Much work has focused on phase transitions in complex networks in which the system transitions from a resilient to a failed state. Furthermore, many of these networks have a community structure, whose effects on resilience have not yet been fully understood. Here, we show that the community structure can significantly affect the resilience of the system in that it removes the phase transition present in a single module, and the network remains resilient at this transition. Our findings provide insight into the resilience of many modular complex systems and clarify the important effects that community structure has on network resilience. (Abstract)

Dorogovtsev, Sergey. Lectures on Complex Networks. Oxford: Oxford University Press, 2010. In an Oxford Master Series in Statistical, Computational, and Theoretical Physics, the University of Aveiro, Portugal, and Ioffe Institute, Russia, scientist, after a decade of research studies, publications (search), and classroom presentations, pens this overview tutorial. Its contents run from classical graphs and small worlds to nets everywhere from cells to the Internet. From many cases can accrue generic self-organization, cross correlations, weightings, motifs, communities, traffic, systemic interactions, and so on. All told another sign that this window upon a dynamic nature, just a decade on, is reaching a robust maturity. Compare with Jordan, Estrada, Li and Peng, Costa, and others herein as the waxing discovery of a creative organic cosmos.

Eom, Young-Ho, et al. Network-based Model of the Growth of Termite Nests. Physical Review E. 92/062810, 2015. Complexity theorists with postings in Italy, France, Finland, and the USA, including Santo Fortunato and Guy Theraulaz at once detail how network phenomena can help quantify how, for example, social insect build and maintain niche constructions. It is then recognized that this facility implies nature’s avail of a universally recurrent geometry everywhere.

We present a model for the growth of the transportation network inside nests of the social insect subfamily Termitinae (Isoptera, termitidae). The model based on the empirical analysis of the real nest networks combined with pruning and a memory effect successfully predicts emergent nest properties. Our results provide an example of how complex network organization and efficient network properties can be generated from simple building rules based on local interactions and contribute to our understanding of the mechanisms that come into play for the formation of termite networks and of biological transportation networks in general. (Abstract excerpts)

Faccin, Mauro, et al. Community Detection in Quantum Networks. arXiv:1310.6638. Theorists from Torino, Barcelona, and Oxford including Jacob Biamonte, continue the reinvention and integration of these depths by way of macro complex systems theories. From many aspects, over the past years, by picking up on information qualities, such subatomic activities are found to contain the same nonlinear forms and dynamics as everywhere else, the nested cosmos becomes one whole again. Mauro Faccin and others are planning a Quantum Frontiers in Network Science symposia at the large NetSci conference in June 2014 at UC Berkeley (Google). See also Degree Distribution in Quantum Walks on Complex Networks by Faccin, et al, at arXiv:1305.6078.

Fayez-Aziz, Muhammad, et al. The Early History and Emergence of Molecular Functions and Modular Scale-Free Network Behavior. Nature Scientific Reports. 6/25058, 2016. With coauthors Kelsey and Gustavo Caetano-Anolles, University of Illinois, Evolutionary Bioinformatics Laboratory researchers contend that these common interactive topologies are similarly in place for life’s original occasion. These propensities occur along with biochemical, nucleotide and protocell rudiments, which they proceed to organize and activate. And the authors see fit to introduce the article with Jorge Borges, as the second quote conveys.

The formation of protein structural domains requires that biochemical functions, defined by conserved amino acid sequence motifs, be embedded into a structural scaffold. Here we trace domain history onto a bipartite network of elementary functional loop sequences and domain structures defined at the fold superfamily level of SCOP classification. The resulting ‘elementary functionome’ network and its loop motif and structural domain graph projections create evolutionary ‘waterfalls’ describing the emergence of primordial functions. They also uncover a dynamics of modular motif embedding in domain structures that is ongoing, which transfers ‘preferential’ cooption properties of ancient loops to emerging domains. Remarkably, we find that the emergence of molecular functions induces hierarchical modularity and power law behavior in network evolution as the network of motifs and structures expand metabolic pathways and translation. (Abstract)

“… I saw the Aleph from every vantage point, I saw in the Aleph the earth and in the earth again the Aleph, I saw my face and viscera, I saw your face and in vertigo I wept, for my eyes had seen that secret and conjectural object whose name is usurped by men …”— Jorge Luis Borges, The Aleph and Other Stories.

Filan, Daniel, et al. Neural Networks are Surprisingly Modular. arXiv:2003.04881. UC Berkeley and Boston University computer engineers find a way to emphasize and increase the practical presence of these local, clustered concentrations of specific cognitive functions in net topologies, just as biological systems draw upon nested modularities for their development and sustenance. Once again, the tacit assumption is a ready transferability of this independent, iconic source as manifest in connectomic and genomic phenomena.

The learned weights of a neural network are often considered devoid of scrutable internal structure. In order to discern structure in these weights, we introduce a measurable notion of modularity for multi-layer perceptrons (MLPs), and investigate their modular structure as trained on datasets of small images. A "module" as we conceive, is a set of neurons with strong internal connectivity but weak external connectivity. We find that MLPs that undergo training and weight pruning are significantly more modular than random networks. (Abstract excerpt)

Fortunato, Santo and Darko Hric. Community Detection in Networks. Physics Reports. 659/1, 2016. This “user’s guide” tutorial by Indiana University, and Aalto University, Finland, systems theorists contributes to Albert Barabasi’s “network revolution” (2012). The effort resides in the genre of distilling independent, generic properties of their universally recurrent node and link dynamic topologies from cosmic to social media. A salient tendency is to form subscales of modular communities within newly perceived multiplexities. Whether body or brain, an organized viability is achieved by nested whole units due to a reciprocity of elemental dots and integral connections.

Garcia-Perez, Guillermo, et al. Multiscale Unfolding of Real Networks by Geometric Renormalization. Nature Physics. 14/6, 2018. University of Barcelona systems theorists Garcia-Perez, Marian Boguna, and Angeles Serrano find this physical and mathematical theory helps tease out inherent regularities across multiplex webworks. This deep conception, while naturally apt, does strain attempts to explain it. A Critical History of Renormalization by Kerson Huang at arXiv:1310.5533, written as a tribute to Nobel laureate Kenneth Wilson (1936-2013), is a good entry. See also Mutual Information, Neural Networks and the Renormalization Group by Koch-Janusz and Ringel (2018 search). We also quote from Wikipedia.

Symmetries in physical theories denote invariance under some transformation, such as self-similarity under a change of scale. The renormalization group provides a powerful framework to study these symmetries, leading to a better understanding of the universal properties of phase transitions. However, the small-world property of complex networks complicates application of the renormalization group. Here, we provide a framework for the investigation of complex networks at different resolutions. We find that real scale-free networks show geometric scaling under this renormalization group transformation. This in turn offers a basis for exploring critical phenomena and universality in complex networks. (Abstract)

In theoretical physics, the renormalization group refers to a mathematical apparatus that allows systematic investigation of the changes of a physical system as viewed at different scales. In particle physics, it reflects the changes in the underlying force laws (codified in a quantum field theory) as the energy scale at which physical processes occur varies, energy/momentum and resolution distance scales being effectively conjugate under the uncertainty principle. A change in scale is called a scale transformation. The renormalization group is intimately related to scale invariance and conformal invariance, symmetries in which a system appears the same at all scales (self-similarity). (Wikipedia)

Gershenson, Carlos and Mikhail Prokopenko. Complex Networks. Artificial Life. Online July, 2011. An Introduction for a forthcoming issue of eight articles on the title subject drawn from the 2010 ALife XI conference in Odense, Denmark. Click on Early Access at the journal’s MIT Press site to view abstracts. Indeed in the past decade a sudden, revolutionary realization has occurred that every physical, organismic, neural, ecological, linguistic and societal domain is distinguished by the same vital nested, invariant, systemic networks. In this regard, the authors cite number of recent books in further support of this natural propensity: Reuven Cohen and Shlomo Havlin Complex Networks: Structure, Robustness and Function (Cambridge, 2010); Mark Newman Networks: An Introduction (Oxford, 2011); one could add Mark Buchanan, et al, eds. Networks in Cell Biology (Cambridge, 2010).

Gosak, Marko, et al. Network Science of Biological Systems at Different Scales. Physics of Life Reviews. Online November, 2017. Akin to Khaluf 2017 who cite a common scale invariance, seven University of Maribor, Slovenia researchers from physics, mathematics, physiology and medical departments including Matjaz Perc report a similar maturation of network studies whence multiplex entity nodes and relational links are found across life’s bacterial, cellular and multicellular phases. Along with all the discrete components (mitochondria, eukaryotes, organisms), equally present interconnections serve an integral communicative physiologies. Once again, the same universal, independent geometries and dynamics animate everywhere.

Network science is today established as a backbone for description of structure and function of various physical, chemical, biological, technological, and social systems. Here we review recent advances in the study of complex biological systems that were inspired and enabled by methods of network science. First, we present research highlights ranging from determination of the molecular interaction network within a cell to studies of architectural and functional properties of brain networks and biological transportation networks. Second, we focus on synergies between network science and data analysis, which enable us to determine functional connectivity patterns in multicellular systems. Third, we concentrate on the emerging field of multilayer networks and review the first endeavors and novel perspectives offered by this framework in exploring biological complexity. (Abstract excerpts)

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