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

1. Network Physics: A Vital Interlinked Anatomy and Physiology

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, Jia-Rong, 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 community-detection 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 meta-data and community structure in real-world networks needs to be re-examined. As a byproduct we find that the exclusive modularity, which introduces a null model based on the degree-corrected 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 low-dimensional 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 in-and out-degree 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 Multi-Fractal 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 self-similarity 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 multi-fractal analysis quantifies the spatial and temporal irregularities of the structural and dynamical formation of complex networks. Despite its effectiveness in unweighted networks, the multi-fractal 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 multi-fractal 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 multi-fractal scaling and (ii) the multi-fractal scaling can be localized in both space and scales. In addition, this multi-fractal characterization framework enables the construction of a scaling-based 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 well-characterized connectomes. (Abstract excerpt)

Yan, Koon-Kiu, et al. Cross-Disciplinary 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 cross-disciplinary 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 day-to-day 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)

Cell Systems is a broad, multidisciplinary monthly journal for outstanding research that provides, supports, or applies systems-level understanding in the life sciences and related disciplines. The journal’s scope includes systems at all scales—from molecules, pathways, cells, and tissues to whole organisms, populations, and ecosystems—and a diversity of traditional disciplines, including but not limited to microbiology, cancer, immunology, plant biology, computational biology, genomics, proteomics, translational medicine, digital healthcare, biological engineering, and systems and synthetic biology.

Yang, Chun-Lin 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 time-dependent parameters. Whether a dynamic complex network is evolving toward synchronization or collapsing can be found by tracking ensemble entropy in time.

Yang, Ruochen, et al. Hidden Network Generating Rules from Partially Observed Complex Networks. Communications Physics. 4/199, 2021. USC (RY and Paul Bogdan) and University of Wisconsin (Frederic Sala) systems theorists (search PB) continue to discern and quantify the presence of common self-organized structural dynamics (as the abstract notes) at each and every natural and human phase. Herein an advanced method is described by which to better elucidate their node/link multiplex occurrence. See also in this journal Unified Treatment of Synchronization Patterns in Generalized Networks with Higher-order, Multilayer, and Temporal Interactions by Yuanzhao Zhang, et al (4/195, 2021).

Complex biological, neuroscience, geoscience and social networks exhibit heterogeneous self-similar higher order structures that are usually characterized as multifractal in nature. However, describing their topologies by a mathematical description and deciphering their governing rules has been elusive and prevents a comprehensive understanding of their networks. Here, we propose a weighted graph model which can reveal the underlying generating rules of complex systems and characterize their node heterogeneity and pairwise interactions. The proposed network generator framework is able to reproduce network properties, differentiate varying structures in brain networks and chromosomal interactions, and detect topologically associating domain regions in conformation maps of the human genome. (Abstract excerpt)

Yeung, Chi Ho and David Saad. Networking – A Statistical Physics Perspective. Journal of Physics A: Mathematical and Theoretical. 46/10, 2013. Nonlinearity and Complexity Research Group, Aston University, Birmingham, UK researchers offer a Topical Review of the many junctures of this ubiquitous biological propensity with an inherently dynamic physical reality. Once again life’s roots are found to run deeper into an increasingly fertile natural ground. As the second quote avers, while not yet seen akin to a Systems Physics, an epochal revolution is underway.

Networking encompasses a variety of tasks related to the communication of information on networks; it has a substantial economic and societal impact on a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption requires new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with nonlinear large-scale systems. This review aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. (Abstract)

Zhang, Zhang et al. Coarse-graining network flow through statistical physics and machine learning.. Nature Communications. 16/1605, 2025. We cite this entry by Beijing Normal University, Indiana University and University of Padua theorists including Manlio De Domenico as an example of new abilities to root complex system phenomena in deep physical substrates by way of an AI assistance.

Information dynamics plays a crucial role in complex systems from cells to societies. Recent advances in statistical physics have been able to find key network properties but large system sizes have computational issues. We use graph neural networks to identify coarse-graining groups to achieve a low computational complexity for practical applications. Our method offers multiscale compression perspective that preserves information flow in biological, social, and technological networks better than other methods mostly focused on network structure. (Excerpt)

Zheng, Bojin, et al. A Simple Model Clarifies the Complicated Relationships of Complex Networks. Nature Scientific Reports. 4/6197, 2014. As the Abstract details, a research team from South-Central University for Nationalities, Wuhan, Beijing University of Posts and Telecommunications, Wuhan University, and Tsinghua University, contend that an understanding of ubiquitous natural nets is best achieved by an emphasis on optimizing or maximizing functions. By such insights, one might add their dynamic activities are Darwinian, Bayesian and algorithmic-like.

Real-world networks such as the Internet and WWW have many common traits. Until now, hundreds of models were proposed to characterize these traits for understanding the networks. Because different models used very different mechanisms, it is widely believed that these traits origin from different causes. However, we find that a simple model based on optimisation can produce many traits, including scale-free, small-world, ultra small-world, Delta-distribution, compact, fractal, regular and random networks. Moreover, by revising the proposed model, the community-structure networks are generated. By this model and the revised versions, the complicated relationships of complex networks are illustrated. The model brings a new universal perspective to the understanding of complex networks and provide a universal method to model complex networks from the viewpoint of optimisation. (Abstract)

Complex networks have been found to be efficient and effective in illuminating various biological, social, and technological systems, for examples, the Internet, WWW and protein-interaction networks. Through the efforts of many scientists, numerous traits of complex networks, such as the scale-free property, the small-world effect, the community structure and the fractal structure, have been discovered. (1)

Zheng, Minzheng, et al. Multiscale Dynamical Network Mechanisms Underlying Aging of an Online Organism from Birth to Death. Nature Scientific Reports. 8/3552, 2018. University of Miami physicists including Neil Johnson and Pedro Manrique expand their unique studies (search PM) to help quantify a person’s whole life span. As lately possible, it is noted that the same complex system properties apply in many other areas such as neurological deficits and social conflicts. A main measure is the quality of network interconnections, which are here seen to decay in onsets of Alzheimer’s disease.

We present the continuous-time evolution of an online organism network from birth to death which crosses all organizational and temporal scales, from individual components through to the mesoscopic and entire system scale. These continuous-time data reveal a lifespan driven by punctuated, real-time co-evolution of the structural and functional networks. Aging sees these structural and functional networks gradually diverge in terms of their small-worldness and eventually their connectivity. (Abstract)

[Prev Pages]   Previous   | 10 | 11 | 12 | 13 | 14 | 15 | 16  Next