Semi-Adaptive Evolution with Spontaneous Modularity of Half-Chaotic Randomly Growing Autonomous and Open Networks

Up until now, studies of Kauffman network stability have focused on the conditions resulting from the structure of the network. Negative feedbacks have been modeled as ice (nodes that do not change their state) in an ordered phase but this blocks the possibility of breaking out of the range of correct operation. This first, very simplified approximation leads to some incorrect conclusions, e.g., that life is on the edge of chaos. We develop a second approximation, which discovers half-chaos and shows its properties. In previous works, half-chaos has been confirmed in autonomous networks, but only using node function disturbance, which does not change the network structure. Now we examine half-chaos during network growth by adding and removing nodes as a disturbance in autonomous and open networks. In such evolutions controlled by a ‘small change’ of functioning after disturbance, the half-chaos is kept but spontaneous modularity emerges and blurs the picture. Half-chaos is a state to be expected in most of the real systems studied, therefore the determinants of the variability that maintains the half-chaos are particularly important in the application of complex network knowledge.

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Structure and function in human and primate social networks: implications for diffusion, network stability and health

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2020.0446 ◽

2020 ◽

Vol 476 (2240) ◽

pp. 20200446 ◽

Cited By ~ 1

Author(s):

R. I. M. Dunbar

Keyword(s):

Social Networks ◽

Physical Health ◽

Health Risks ◽

Network Structure ◽

Fractal Structure ◽

Social World ◽

Structure And Function ◽

Network Stability ◽

External Threats ◽

And Function

The human social world is orders of magnitude smaller than our highly urbanized world might lead us to suppose. In addition, human social networks have a very distinct fractal structure similar to that observed in other primates. In part, this reflects a cognitive constraint, and in part a time constraint, on the capacity for interaction. Structured networks of this kind have a significant effect on the rates of transmission of both disease and information. Because the cognitive mechanism underpinning network structure is based on trust, internal and external threats that undermine trust or constrain interaction inevitably result in the fragmentation and restructuring of networks. In contexts where network sizes are smaller, this is likely to have significant impacts on psychological and physical health risks.

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Influence of kinetics of network growth on fibrin network structure

Thrombosis Research ◽

10.1016/0049-3848(86)91410-6 ◽

1986 ◽

Vol 41 ◽

pp. 32

Author(s):

G.A. Shah ◽

C.H. Nair ◽

D.P. Dhall

Keyword(s):

Network Structure ◽

Network Growth ◽

Fibrin Network ◽

Kinetics Of ◽

Fibrin Network Structure

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STRUCTURAL TENDENCIES — EFFECTS OF ADAPTIVE EVOLUTION OF COMPLEX (CHAOTIC) SYSTEMS

International Journal of Modern Physics C ◽

10.1142/s0129183108012418 ◽

2008 ◽

Vol 19 (04) ◽

pp. 647-664 ◽

Cited By ~ 4

Author(s):

ANDRZEJ GECOW

Keyword(s):

Finite Number ◽

Adaptive Evolution ◽

Boolean Networks ◽

Network Growth ◽

Synchronous Mode ◽

Scale Free ◽

Damage Spreading ◽

Simplified Algorithm ◽

Complex Chaotic Systems ◽

Statistical Effects

We describe systems using Kauffman and similar networks. They are directed functioning networks consisting of finite number of nodes with finite number of discrete states evaluated in synchronous mode of discrete time. In this paper we introduce the notion and phenomenon of "structural tendencies". Along the way we expand Kauffman networks, which were a synonym of Boolean networks, to more than two signal variants and we find a phenomenon during network growth which we interpret as "complexity threshold". For simulation we define a simplified algorithm which allows us to omit the problem of periodic attractors. We estimate that living and human designed systems are chaotic (in Kauffman sense) which can be named — complex. Such systems grow in adaptive evolution. These two simple assumptions lead to certain statistical effects, i.e., structural tendencies observed in classic biology but still not explained and not investigated on theoretical way. For example, terminal modifications or terminal predominance of additions where terminal means: near system outputs. We introduce more than two equally probable variants of signal, therefore our networks generally are not Boolean networks. They grow randomly by additions and removals of nodes imposed on Darwinian elimination. Fitness is defined on external outputs of system. During growth of the system we observe a phase transition to chaos (threshold of complexity) in damage spreading. Above this threshold we identify mechanisms of structural tendencies which we investigate in simulation for a few different networks types, including scale-free BA networks.

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Emergence of Matured Chaos During Network Growth, Place for Adaptive Evolution and More of Equally Probable Signal Variants as an Alternative to Bias p

Chaotic Systems ◽

10.5772/14485 ◽

2011 ◽

Author(s):

Andrzej Gecow

Keyword(s):

Adaptive Evolution ◽

Network Growth

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Regulatory network structure determines patterns of intermolecular epistasis

eLife ◽

10.7554/elife.28921 ◽

2017 ◽

Vol 6 ◽

Cited By ~ 6

Author(s):

Mato Lagator ◽

Srdjan Sarikas ◽

Hande Acar ◽

Jonathan P Bollback ◽

Călin C Guet

Keyword(s):

Transcription Factor ◽

Adaptive Evolution ◽

Network Structure ◽

Phenotypic Variation ◽

Regulatory Network ◽

Individual Component ◽

Regulatory System ◽

Molecular Systems ◽

Transcriptional Regulatory ◽

Dna Binding Site

Most phenotypes are determined by molecular systems composed of specifically interacting molecules. However, unlike for individual components, little is known about the distributions of mutational effects of molecular systems as a whole. We ask how the distribution of mutational effects of a transcriptional regulatory system differs from the distributions of its components, by first independently, and then simultaneously, mutating a transcription factor and the associated promoter it represses. We find that the system distribution exhibits increased phenotypic variation compared to individual component distributions - an effect arising from intermolecular epistasis between the transcription factor and its DNA-binding site. In large part, this epistasis can be qualitatively attributed to the structure of the transcriptional regulatory system and could therefore be a common feature in prokaryotes. Counter-intuitively, intermolecular epistasis can alleviate the constraints of individual components, thereby increasing phenotypic variation that selection could act on and facilitating adaptive evolution.

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Network analysis of depressive symptoms in Hong Kong residents during the COVID-19 pandemic

Translational Psychiatry ◽

10.1038/s41398-021-01543-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Teris Cheung ◽

Yu Jin ◽

Simon Lam ◽

Zhaohui Su ◽

Brian J. Hall ◽

...

Keyword(s):

Hong Kong ◽

Depressive Symptoms ◽

Depressive Symptom ◽

Network Structure ◽

Network Stability ◽

Patient Health ◽

Males And Females ◽

Sad Mood ◽

Global Strength ◽

Community Adult

AbstractIn network theory depression is conceptualized as a complex network of individual symptoms that influence each other, and central symptoms in the network have the greatest impact on other symptoms. Clinical features of depression are largely determined by sociocultural context. No previous study examined the network structure of depressive symptoms in Hong Kong residents. The aim of this study was to characterize the depressive symptom network structure in a community adult sample in Hong Kong during the COVID-19 pandemic. A total of 11,072 participants were recruited between 24 March and 20 April 2020. Depressive symptoms were measured using the Patient Health Questionnaire-9. The network structure of depressive symptoms was characterized, and indices of “strength”, “betweenness”, and “closeness” were used to identify symptoms central to the network. Network stability was examined using a case-dropping bootstrap procedure. Guilt, Sad Mood, and Energy symptoms had the highest centrality values. In contrast, Concentration, Suicide, and Sleep had lower centrality values. There were no significant differences in network global strength (p = 0.259), distribution of edge weights (p = 0.73) and individual edge weights (all p values > 0.05 after Holm–Bonferroni corrections) between males and females. Guilt, Sad Mood, and Energy symptoms were central in the depressive symptom network. These central symptoms may be targets for focused treatments and future psychological and neurobiological research to gain novel insight into depression.

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Evolution through bursts: Network structure develops through localized bursts in time and space

Network Science ◽

10.1017/nws.2016.13 ◽

2016 ◽

Vol 4 (3) ◽

pp. 293-313 ◽

Cited By ~ 3

Author(s):

HILLA BROT ◽

LEV MUCHNIK ◽

JACOB GOLDENBERG ◽

YORAM LOUZOUN

Keyword(s):

Network Structure ◽

Network Models ◽

Network Evolution ◽

Stochastic Network ◽

Implicit Assumption ◽

Network Ties ◽

Network Growth ◽

Network Change ◽

Local Clustering ◽

Tightly Coupled

Models of network evolution are based on the implicit assumption that network growth is continuous, uniform, and steady. Using the data collected from a large online-blogging platform, we show that the addition and removal of network ties by users do not occur sporadically at isolated nodes spread all over the network, as assumed by the vast majority of stochastic network models, but rather occur in brief bursts of intense local activity.These bursts of network growth and attrition (addition and removal of network ties) are highly localized around focal nodes. Such network changes coincide with nearly instantaneous densification of the ties between the affected nodes, resulting in an increase of local clustering. Furthermore, we find that these network changes are tightly coupled to the dynamics of individual attributes, particularly the increase in homology between neighboring nodes (homophily) within the scope of the burst. Coincidence of the localized network change with the increase in homophily suggests a strong coupling between the selection and influence processes that lead to simultaneous elevation of assortativity and clustering.

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