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

1. Network Physics: A Vital Interlinked Anatomy and Physiology

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.

Dorogovtsev, Sergey and Jose Mendes. The Nature of Complex Networks. Oxford: Oxford University Press, 2022. University of Aveiro, Portugal systems physicists proceed with a thorough introduction to network phenomena by way of a novel synthesis with statistical mechanics. As a result these disparate fields are current seen to be lately finding similar themes and common ground.

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.

Fortunato, Santo and Mark Newman. 20 Years of Network Community Detection. arXiv:2208.0111. Senior University of Indiana and University of Michigan systems theorists (search each) provide insider insights to findings and clarifications on the way to appreciating how nature’s ubiquitous interconnectivity proceeds to join into dynamic communal units.

A fundamental technical challenge in the analysis of network data is the automated discovery of communities - groups of nodes that are strongly connected or that share similar features or roles. In this commentary we review progress in the field over the last 20 years.

Fronczak, Agata, et al.. Scaling Theory of Fractal Complex Networks: Bridging Local Self-Similarity and Global Scale-Invariance. arXiv:2306.13751. Warsaw University of Technology physicists advance a latest quantification of nature’s structural consistency as an infinite repetition in exemplary kind from universe to humanverse. See also Fractal Dimensions of Networks by Eric Rosenberg for another treatment. We then make note that by midyear this perennial above/below, macro/micro ecosmic has well gained a scientific veracity as a phenomenal revelation. See also A General Model of Hierarchical Fractal Scale-Free Networks y Kousuke Yakubo and Yuka Fujiki in PLoS One (March 21. 2022) for another record of nature's infinite regularity.

We show that fractality in complex networks arises from the geometric self-similarity of their built-in hierarchical community-like mathematical structure. This box-based approach drawn from both phase transition scales and renormalization group theory leads to fractal deep analogies. We successfully verify our findings in real networks situated in various fields (information - the World Wide Web, biological - the human brain, and social - scientific collaborations). (Excerpt)

Frottier, Theo, et al. Harmonic Structures of Beethoven Quarters: A Complex Network Approach. arXiv:2201.08796. Into these 2020s, system physicists TF and Bertrand Georgeot, University of Toulouse and Olivier Giraud, University of Paris provide novel insights into how even musical compositions are distinguished by a common interconnective topology. As we may perceive, a universal anatomy and physiology, a music of the universe and humanverse, is just now being revealed. See also How Network-based Approaches can Complement Studies in Dementia by Cemile Kocoglu, et al in Trends in Genetics. (38/9, 2022) for another example.

We propose a complex network approach to the harmonic structure which underlies western tonal music. From a database of Beethoven's string quartets, we construct a directed network whose nodes are musical chords and edges connect chords. We show that the network is scale-free and has specific properties when ranking algorithms are applied. We explore its community structure and musical interpretation, and propose statistical measures from network theory to distinguish stylistically between periods of composition. (Excerpt)

The present work shows that the complex network approach can be fruitfully applied to the harmonic structure of musical works. Based on the example of Beethoven string quartets, we specified the properties of this new type of networks, and in particular we discussed the relationship between the spectrum of the Google matrix, the community structures, and musical specificities of the scores. (6)

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