
IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic CodeScript Source1. Network Physics: A Vital Interlinked Anatomy and Physiology Wang, Wei, et al. Coevolution Spreading in Complex Networks. arXiv: 1901.02125. In a 115 page paper with 334 references, informatics researchers based at the University of Electronic and Technology of China apply the latest complexity theories to further quantify this vital phase of composite social behavior, disease, health, and other aspects. The paper now appears in Physics Reports. (Online July 29, 2019). The propagations of diseases, behaviors and information in real systems are rarely independent of each other, for they coevolve with strong interactions. The study of dynamic spatiotemporal patterns and critical phenomena of networked coevolution spreading can provide theoretical foundations to control epidemics, predict collective behaviors in social systems, and so on. In this review, we draw upon the perspectives of statistical mechanics and network science such as critical phenomena, phase transitions, interacting mechanisms, and network topology for four representative types of biological contagions, social contagions, epidemic–awareness, and epidemic–resources. (Abstract excerpt) Wang, WenXu, et al. Universal Dynamics on Complex Networks. EPL Europhysics Letters. 87/18006, 2009. An important distinction can be made with regard to complex system phenomena, whose study can be seen to take two approaches. The majority of efforts consider their manifest evidence across nature from galaxies to genomes to global societies. Their other mode, as increasingly noted in physics journals in sections such as Interdisciplinary Studies or Soft Matter, is to distill common features which seem to be in similar effect everywhere. As a latter example, Chinese American scientists at Arizona State University report how a generic pattern and process can be gleaned from “realworld networks, whether biological, physical, technological or social.” One may then muse that these dual implicate or explicate phases exemplify the independent presence and phenotype impression of a genetic code. In particular, given networks from different contexts, is there a universal class of dynamics that absolutely has no dependence on structural details of the network? Here we provide a surprising but an affirmative answer to the above question. In particular, we find the existence of weighting schemes for which the details of various realworld networks, whether biological, technological or social, have little influence on typical dynamical processes such as synchronization, epidemic spreading, and percolation. (18006) Wang, Yafeng, et al. Growth, Collapse, and SelfOrganized Criticality in Complex Networks. Nature Scientific Reports. 6/24445, 2016. Shaanxi Normal University, Zhejiang University, and Arizona State University physicists describe how the tendency of nonlinear phase transitions to be poised between order and chaos can similarly characterize network dynamics. Since this is phenomena now known to occur everywhere in nature and society, such an independent representation is increasingly possible and important. Network growth is ubiquitous in nature (e.g., biological networks) and technological systems (e.g., modern infrastructures). To understand how certain dynamical behaviors can or cannot persist as the underlying network grows is a problem of increasing importance in complex dynamical systems as well as sustainability science and engineering. We address the question of whether a complex network of nonlinear oscillators can maintain its synchronization stability as it expands. We find that a large scale avalanche over the entire network can be triggered in the sense that the individual nodal dynamics diverges from the synchronous state in a cascading manner within a relatively short time period. In particular, after an initial stage of linear growth, the network typically evolves into a critical state where the addition of a single new node can cause a group of nodes to lose synchronization, leading to synchronization collapse for the entire network. A statistical analysis reveals that the collapse size is approximately algebraically distributed, indicating the emergence of selforganized criticality. (Abstract) Wang, Zhen, et al. Evolutionary Games on Multilayer Networks. arXiv:1504.04359. An introduction to a special issue of the European Physical Journal B, by an international team, including Matjaz Perc, with postings in China, Hungary, Slovenia, and Saudi Arabia. In regard, the paper surveys the progress of complexity science from the late 1980s to today. As the quote advises, nature’s creative course by which many discrete agents arrange into viable collectives is seen as most distinguished by interlinking network topologies. A novel reality is thus revealed and quantified of organically nested systems which repeat the same patterns and dynamics at every strata and species. It is then stated that keen insights can be gained if this developmental phenomena is seen as a strategic, decisionmaking game activity. Wellnitz, David, et al. A Network Approach to Atomic Spectra.. Journal of Physics: Complexity. July, 2023. University of Strasbourg, Heidelberg and Konstanz, along with MPI Intelligent Systems researchers report that even these quantum material depths can be found to exhibit and hold to nature's common node/link netwise dynamic animations. Network science provides a universal framework for modeling complex systems, contrasting the reductionist approach generally adopted in physics. In a prototypical study, we utilize network models created from spectroscopic data of atoms to predict microscopic properties of the underlying physical system. For simple atoms such as helium, an a posteriori inspection of spectroscopic network communities reveals the emergence of quantum numbers and symmetries. For more complex atoms such as thorium, finer network hierarchies suggest additional microscopic symmetries or configurations. Our work promotes a genuine bidirectional exchange of methodology between network science and physics, and presents new perspectives for the study of atomic spectra. West, Michael. Why do Galaxies Align? Astronomy. May, 2018. We record this popular survey by the deputy director for science at the Lowell Observatory in Flagstaff, Arizona because it illustrates an inherent tendency of even starry galaxies to form filamentary network, a cosmic web, akin to organic physiologies and colonies. Xie, JiaRong, et al. Completeness of Community Structure in Networks. Nature Scientific Reports. 7/5269, 2017. We note because University of Science and Technology of China, Hefei, Chinese Academy of Sciences, and Anhui University, China physicists proceed to give these ubiquitous modular forms a basis in statistical physics phenomena. By defining a new measure to community structure, exclusive modularity, and based on cavity method of statistical physics, we develop a mathematically principled method to determine the completeness of community structure, which represents whether a partition that is either annotated by experts or given by a communitydetection algorithm, carries complete information about community structure in the network. Our results demonstrate that the expert partition is surprisingly incomplete in some networks such as the famous political blogs network, indicating that the relation between metadata and community structure in realworld networks needs to be reexamined. As a byproduct we find that the exclusive modularity, which introduces a null model based on the degreecorrected stochastic block model, is of independent interest. We discuss its applications as principled ways of detecting hidden structures, finding hierarchical structures without removing edges, and obtaining lowdimensional embedding of networks. (Abstract) Xie, Zheng, et al. Modeling the Citation Network by Network Cosmology. PLoS One. March 25, 2015. Chinese mathematicians and computer scientists proceed to quantify publication reference webs by way of a widest parallel with the cosmic network proposals of Dmitri Krioukov and colleagues (search DK, Barabasi, Rosetta Cosmos, Network Physics). As we have noted, by these expansive correlations, an intrinsically textual narrative nature becomes evident once again. Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar inand outdegree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well. (Abstract) Xue, Yuankun and Paul Bogdan. Reliable MultiFractal Characterization of Weighted Complex Networks. Nature Scientific Reports. 7/7487, 2017. As the Abstract cites, University of Southern California engineers finesse algorithmic studies of active systems by way of temporal and spatial interactions. The paper illumes a selfsimilarity as a common feature of network topologies, which is extended into neural net, machine learning domains. See also A Statistical Physics Characterization of the Complex Systems Dynamics by Hana Koorehdavoudi and Paul Bogdan in the same journal (6/27602, 2016). Through an elegant geometrical interpretation, the multifractal analysis quantifies the spatial and temporal irregularities of the structural and dynamical formation of complex networks. Despite its effectiveness in unweighted networks, the multifractal geometry of weighted complex networks, the role of interaction intensity, the influence of the embedding metric spaces and the design of reliable estimation algorithms remain open challenges. To address these challenges, we present a set of reliable multifractal estimation algorithms for quantifying the structural complexity and heterogeneity of weighted complex networks. Our methodology uncovers that (i) the weights of complex networks and their underlying metric spaces play a key role in dictating the existence of multifractal scaling and (ii) the multifractal scaling can be localized in both space and scales. In addition, this multifractal characterization framework enables the construction of a scalingbased similarity metric and the identification of community structure of human brain connectome. The detected communities are accurately aligned with the biological brain connectivity patterns. (Abstract) Yan, Gang, et al. Network Control Principles Predict Neuron Function in the Caenorhabditis elegans Connectome. Nature. 550/519, 2017. A team of Northeastern University, Cambridge University, and Dana Farber Cancer Institute researchers including Albert Laszlo Barabasi contribute another way that neural networks are similarily serve the sensory apparatus of this rudimentary creature. Thus across life’s cerebral evolution, the same “neurome” (my word) seems to manifestly advise and inform as it ascends in kind to our reconstructive worldwise sapience. Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure–function relationship in biological, social, and technological network. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less wellcharacterized connectomes. (Abstract excerpt) Yan, KoonKiu, et al. CrossDisciplinary Network Comparison: Matchmaking between Hairballs. Cell Systems. 2/3, 2016. In response to the proliferation of node and link topologies across every natural and social domain since 2000, Yale University computational biology faculty and colleagues, including Mark Gerstein, propose a common integration to aid understandings. The paper opens by noting that such a synthesis would benefit a similar burst of –omic suffixes. It then shows how many neural, metabolic, regulatory, software, circuitry, Internet, transport, ecological, epidemic, areas express the same occasions, modularity, and geometry. Biological systems are complex. In particular, the interactions between molecular components often form dense networks that, more often than not, are criticized for being inscrutable ‘‘hairballs.’’ We argue that one way of untangling these hairballs is through crossdisciplinary network comparison—leveraging advances in other disciplines to obtain new biological insights. In some cases, such comparisons enable the direct transfer of mathematical formalism between disciplines, precisely describing the abstract associations between entities and allowing us to apply a variety of sophisticated formalisms to biology. In cases in which the detailed structure of the network does not permit the transfer of complete formalisms between disciplines, comparison of mechanistic interactions in systems for which we have significant daytoday experience can provide analogies for interpreting relatively more abstruse biological networks. Here, we illustrate how these comparisons benefit the field with a few specific examples related to network growth, organizational hierarchies, and the evolution of adaptive systems. (Abstract) Yang, ChunLin and Steve Suh. A General Framework for Dynamic Complex Networks. Journal of Vibration Testing and System Dynamics. 5/1, 2021. Texas A & M University bioengineers (search Suh)conceive further ways to describe and quantify life’s deep proclivity for such node/link multiples anatomies and physiologies. A novel approach by which to define natural networks is presented which 1) contains a Kuramoto model to define constituent dynamics, 2) explores information entropy to describe global ensemble behaviors, 3) defines the variation of the state of elements using energy, and 4) introduces two timedependent parameters. Whether a dynamic complex network is evolving toward synchronization or collapsing can be found by tracking ensemble entropy in time.
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