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Establishing a mapping between the emergent biological properties and the repository of network structures has been of great relevance in systems and synthetic biology. Adaptation is one such biological property of paramount importance that promotes regulation in the presence of environmental disturbances. This paper presents a nonlinear systems theory-driven framework to identify the design principles for perfect adaptation with respect to external disturbances of arbitrary magnitude. Based on the prior information about the network, we frame precise mathematical conditions for adaptation using nonlinear systems theory. We first deduce the mathematical conditions for perfect adaptation for constant input disturbances. Subsequently, we translate these conditions to specific necessary structural requirements for adaptation in networks of small size and then extend to argue that there exist only two classes of architectures for a network of any size that can provide local adaptation in the entire state space, namely, incoherent feed-forward (IFF) structure and negative feedback loop with buffer node (NFB). The additional positiveness constraints further narrow the admissible set of network structures. This also aids in establishing the global asymptotic stability for the steady state given a constant input disturbance. The proposed method does not assume any explicit knowledge of the underlying rate kinetics, barring some minimal assumptions. Finally, we also discuss the infeasibility of certain IFF networks in providing adaptation in the presence of downstream connections. Moreover, we propose a generic and novel algorithm based on non-linear systems theory to unravel the design principles for global adaptation. Detailed and extensive simulation studies corroborate the theoretical findings. [ABSTRACT FROM AUTHOR]

In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that occurred a long time ago–a phenomenon known as "memory decay". Previous studies either predefined a short-run and long-run memory or fixed a parametric exponential memory decay using a predefined half-life period. In real-life relational event networks, however, it is generally unknown how the influence of past events fades as time goes by. For this reason, it is not recommendable to fix memory decay in an ad-hoc manner, but instead we should learn the shape of memory decay from the observed data. In this paper, a novel semi-parametric approach based on Bayesian Model Averaging is proposed for learning the shape of the memory decay without requiring any parametric assumptions. The method is applied to relational event history data among socio-political actors in India and a comparison with other relational event models based on predefined memory decays is provided. [ABSTRACT FROM AUTHOR]

The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. $ FRM@Crypto $ FRM @ Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market. [ABSTRACT FROM AUTHOR]

This paper aims to understand how a group of academics cite each others' work through time, considering the simultaneous co-evolution of three networks representing their scientific collaboration, the journals in which they publish and institutional membership. It argues that both social and cognitive processes contribute to these dynamics. Two types of network mechanisms are considered specifically: closures by affiliation and closures by association. To assess whether these mechanisms generate the macro features of the network under study, we propose new features for three-mode multilevel networks such as the mixed geodesic distances, mixed degree distributions, and the mixed quadrilateral census. We investigate whether a micro-level model that considers the above-mentioned network mechanisms is able to correctly reproduce these features. We apply stochastic actor-oriented models (SAOMs) for one-mode and two-mode networks to link the micro-macro processes using a dataset of a scientific community of astronomers from 2013 to 2015. The results suggest that social relationships grounded on scientific collaboration and proximity based on institutional affiliation are more accurately suited to understanding the co-evolution of the network of citations than an alternative approach that merely considers cognitive-based networks measured as the similarity in publishing in the same journals. • We investigate how a group of academics cite each others' work through time considering the co-evolution of a multilevel network. • The co-evolution of a three-mode multilevel network is analysed considering cross-level effects. • New and already available measures for diagnostics are used for statistical models for social networks to identify how micro-mechanisms trigger different structures at the macro level. • Social relationships grounded on scientific collaboration and space proximity based on institutional affiliation are more accurately suited to understand the co-evolution of the networks in a scientific network. [ABSTRACT FROM AUTHOR]

Abstract: Understanding cascading processes on complex network topologies is paramount for modelling how diseases, information, fake news and other media spread. In this article, we extend the multi-type branching process method developed in Keating et al., (2022), which relies on networks having homogenous node properties, to a more general class of clustered networks. Using a model of socially inspired complex contagion we obtain results, not just for the average behaviour of the cascades but for full distributions of the cascade properties. We introduce a new method for the inversion of probability generating functions to recover their underlying probability distributions; this derivation naturally extends to higher dimensions. This inversion technique is used along with the multi-type branching process to obtain univariate and bivariate distributions of cascade properties. Finally, using clique-cover methods, we apply the methodology to synthetic and real-world networks and compare the theoretical distribution of cascade sizes with the results of extensive numerical simulations.

Network science has made tremendous advances, allowing the modeling of complex real-world systems. Although networks include sophisticated community structures by definition, discovering and understanding these communities remains a challenging endeavor, compounded by the need to navigate a multidisciplinary terrain. This comprehensive survey serves as a contemporary guide and systematically presents an up-to-date survey of community detection methods, meticulously categorizing them for a comprehensive understanding. Through critical analysis, we assess the strengths, weaknesses, and performance metrics of various algorithms, ranging from classical techniques to cutting-edge methods designed to address the complexities of overlapping community detection. Additionally, we explore emerging trends in dynamic community detection, including techniques like Temporal Motif Analysis and Continuous-Time Models. We additionally present an in-depth evaluation of these methods, examining their performance on both artificial benchmarks and real-world networks. This review also sheds light on the diverse applications of community detection across domains such as sociology, biology, education, and technology. By presenting a holistic view of community detection, we aim to facilitate researchers' and practitioners' access to this crucial field, addressing the theoretical and practical application gaps, and contributing to the continued evolution of network science. [ABSTRACT FROM AUTHOR]

The history of the Jewish press can be studied both as a mirror and as a motor. The former looks back on the relationship between history and culture. It lends itself to the purpose of curation and retrieval and implies an agreed Jewish historiography. The latter method is forward looking. It suggests a more holistic approach, reaching beyond the identification of individual titles, and encourages a mapping of the overall Jewish press landscape in terms of cultural and economic capital, marginal initiatives, cross referencing, and the identification of trends. This article embarks on the latter approach. Presupposing that public discourses do not just reflect or represent social entities and alliances, but also construct or constitute them, I focus on the visionary character of Jewish periodicals and on their transformative function. [ABSTRACT FROM AUTHOR]

Heteroclinic structures organize global features of dynamical systems. We analyse whether heteroclinic structures can arise in network dynamics with higher-order interactions which describe the nonlinear interactions between three or more units. We find that while commonly analysed model equations such as network dynamics on undirected hypergraphs may be useful to describe local dynamics such as cluster synchronization, they give rise to obstructions that allow to design of heteroclinic structures in phase space. By contrast, directed hypergraphs break the homogeneity and lead to vector fields that support heteroclinic structures. [ABSTRACT FROM AUTHOR]

Growing interest in network dynamics has led to insights about patterns of network change, drivers of tie formation, and the temporal unfolding of the consequences of networks. To this area of inquiry, we introduce networking speed—the time that it takes for individuals to form a network tie—as an important but so far largely overlooked aspect. We develop a theory of networking speed that explains how different catalysts enable professionals to introduce variation into the speed with which they form interpersonal network ties. We discuss how such variation in the speed with which ties have been formed influences relational outcomes and the network returns that these ties generate. This discussion illustrates that high networking speed can entail advantages as well as pitfalls. We also explore temporal implications of networking speed—for instance, the persistence of the effects of speed over time. Overall, we conceptualize networking speed as a constitutive element of how interpersonal networks function in professional settings, and we propose a future research program for the integration of this novel concept into organizational network research. [ABSTRACT FROM AUTHOR]

INTRODUCTION: We tested whether changes in functional networks predict cognitive decline and conversion from the presymptomatic prodrome to symptomatic disease in familial frontotemporal dementia (FTD). METHODS: For hypothesis generation, 36 participants with behavioral variant FTD (bvFTD) and 34 controls were recruited from one site. For hypothesis testing, we studied 198 symptomatic FTD mutation carriers, 341 presymptomatic mutation carriers, and 329 family members without mutations. We compared functional network dynamics between groups, with clinical severity and with longitudinal clinical progression. RESULTS: We identified a characteristic pattern of dynamic network changes in FTD, which correlated with neuropsychological impairment. Among presymptomatic mutation carriers, this pattern of network dynamics was found to a greater extent in those who subsequently converted to the symptomatic phase. Baseline network dynamic changes predicted future cognitive decline in symptomatic participants and older presymptomatic participants. DISCUSSION: Dynamic network abnormalities in FTD predict cognitive decline and symptomatic conversion. HIGHLIGHTS: We investigated brain network predictors of dementia symptom onset Frontotemporal dementia results in characteristic dynamic network patterns Alterations in network dynamics are associated with neuropsychological impairment Network dynamic changes predict symptomatic conversion in presymptomatic carriers Network dynamic changes are associated with longitudinal cognitive decline.

To what extent does speech and music processing rely on domain-specific and domain-general neural networks? Using whole-brain intracranial EEG recordings in 18 epilepsy patients listening to natural, continuous speech or music, we investigated the presence of frequency-specific and network-level brain activity. We combined it with a statistical approach in which a clear operational distinction is made between shared, preferred, and domain-selective neural responses. We show that the majority of focal and network-level neural activity is shared between speech and music processing. Our data also reveal an absence of anatomical regional selectivity. Instead, domain-selective neural responses are restricted to distributed and frequency-specific coherent oscillations, typical of spectral fingerprints. Our work highlights the importance of considering natural stimuli and brain dynamics in their full complexity to map cognitive and brain functions.

Existing literature has explored how different firm behaviours and strategies in different institutional contexts result in variegation in network structures. Based on those studies, this paper first examines how networks have been shaped by China's institutional transition in general and the process of marketisation in particular. Furthermore, we stress that the rise of networks in China's film industry has in return boosted the development of private firms and subsequently pushed forward the process of marketisation in this industry. Empirical results show that private firms have benefitted more from the complementarity between direct knowledge spillovers via inter‐firm networks and indirect knowledge spillovers via inter‐city networks, while state‐owned enterprises are incapable of coping with the competition effects stemming from inter‐city networks and fail to benefit from trans‐local knowledge spillovers in networks. Our main contribution is thus to direct attention to the interaction between network dynamics and institutional transition, which remains underexplored in the current literature. [ABSTRACT FROM AUTHOR]

Since the 1990s, the European Commission has paid great attention to innovation processes and knowledge transfer, stimulating and financing several research and cooperation programs. Recent geopolitical transformations have shifted attention towards the integrations between the European Member States and the Neighboring Countries. Literature produced many contributions over the past two decades with a focus on knowledge sharing, space dimension, and the role played by each actor involved within networks analyzed, but a lack remains in the field of cross-border cooperation. This research gap is addressed in this paper by investigating the main dynamics that led to new partnerships and networks of collaboration. Spatial proximity, social distance, and the individual characteristics are analyzed taking into account the relations that influence the decision to cooperate. Data are collected considering the first call for proposal of the Interreg IPA CBC Italy Albania-Montenegro Programme, analyzing both the effects of social and geographical distance, beyond the effects of individual characteristics of each partner involved. The results suggest that network effects are present; social closeness is significantly associated with partners' choice and this indicates that connections within a network are influenced by the position that each node assumes; consequently, the network effects are more relevant than the spatial one. [ABSTRACT FROM AUTHOR]

Network dynamics are crucial for action and sensation. Changes in synaptic physiology lead to the reorganization of local microcircuits. Consequently, the functional state of the network impacts the output signal depending on the firing patterns of its units. Networks exhibit steady states in which neurons show various activities, producing many networks with diverse properties. Transitions between network states determine the output signal generated and its functional results. The temporal dynamics of excitation/inhibition allow a shift between states in an operational network. Therefore, a process capable of modulating the dynamics of excitation/inhibition may be functionally important. This process is known as disinhibition. In this review, we describe the effect of GABA levels and GABAB receptors on tonic inhibition, which causes changes (due to disinhibition) in network dynamics, leading to synchronous functional oscillations. [ABSTRACT FROM AUTHOR]

People in the early stages of their careers often face a trade-off between cultivating a closed network that helps them secure the resources they need to survive or developing an open network that can help them succeed. Actors who overcome this trade-off transition from a closed network to an open network; those who fail to do so can be caught in a survival trap that jeopardizes their chances of having a successful career. We identify the factors that enable and constrain network transitions and test our theory on a sample of Korean pop (K-pop) freelance songwriters before they have attained their first commercial hit. These songwriters initially rely on a closed network of collaborators and transition toward an open network by working with fellow songwriters who are not connected to those collaborators. This network transition occurs faster among songwriters who eventually attain their first hit than among those who remain unsuccessful. Songwriters are more likely to collaborate with new distant colleagues when they have a reference group of commercially successful peers and when they have created stylistically similar songs in the past that have failed to become hits. However, most of their new distant colleagues also lack a hit, revealing a status barrier that constrains the network transition of early-career songwriters. [ABSTRACT FROM AUTHOR]

Breathing is a vital rhythmic motor behavior with a surprisingly broad influence on the brain and body. The apparent simplicity of breathing belies a complex neural control system, the breathing central pattern generator (bCPG), that exhibits diverse operational modes to regulate gas exchange and coordinate breathing with an array of behaviors. In this review, we focus on selected advances in our understanding of the bCPG. At the core of the bCPG is the preBötzinger complex (preBötC), which drives inspiratory rhythm via an unexpectedly sophisticated emergent mechanism. Synchronization dynamics underlying preBötC rhythmogenesis imbue the system with robustness and lability. These dynamics are modulated by inputs from throughout the brain and generate rhythmic, patterned activity that is widely distributed. The connectivity and an emerging literature support a link between breathing, emotion, and cognition that is becoming experimentally tractable. These advances bring great potential for elucidating function and dysfunction in breathing and other mammalian neural circuits.

Traffic flow forecasting is a critical task for intelligent transportation systems. However, the existed forecasting can only be conducted at certain time steps, because the data are discretely collected at these time steps. In contrast, traffic flow evolves in real time via a continuous manner in real world. Therefore, an ideal forecasting paradigm should be performed at arbitrary time steps instead of only at these certain time steps. Considering the forecasting time steps will no longer be restricted by these time steps, we call such paradigm as temporal super-resolution forecasting. In this paper, we incorporate the idea of neural ordinary differential equations (neural ODEs) to handle the problem, modeling the change rate of traffic flow on the urban road. Therefore, due to the continuous nature of ordinary differential equations, the traffic flow at arbitrary time steps can be forecasted by performing definite integral for the change rate. The urban road is usually regarded as a network, and the change rate of which can be described by continuous-time network dynamics, we parameterize the network dynamics of the traffic flow to quantify the change rate. On these foundations, we propose spatial-temporal continuous dynamics network to complete the temporal super-resolution forecasting task. Extensive experiments on public traffic flow datasets illustrate that our model can achieve high accuracy on temporal super-resolution forecasting, while ensuring its performance on conventional experimental settings at these certain time steps. [ABSTRACT FROM AUTHOR]

[Display omitted] Taking inspiration from biological information processing in neural assemblies, deep learning and artificial intelligence made solutions to complex problems more feasible. While at first realized by silicon-based hardware, memristive devices for vector-matrix multiplication are attracting huge focus due to their potential integration in large, parallel cross-bar arrays, mimicking the aspect of highly parallel processing (and in-memory processing) in biological neural networks. In real neuron assemblies, however, the connections are not strictly formed in a rigid 2D crossbar array, but form and degrade dynamically in a 3D environment with features of hierarchy, modularity and reconfigurability. To fully explore the capabilities of truly brain-like hardware computing, the transition towards a platform with dynamically reconfigurable connections is mandatory. This work showcases different approaches to address this biological motivation, covering the fields of Topology & Structure as well as Dynamics of biological systems, and classifies them with respect to seven fundamental principles of brain-like computing. The approaches are ranging from highly interconnected nanogranular networks with dynamically reconfigurable connections over liquid-solid composites rearranging connections via dielectrophoresis and guided redox-wiring to the mimicking of neural action potentials by relaxation-type oscillators that are used as input stimuli. [ABSTRACT FROM AUTHOR]

Modeling cell signal transduction pathways via Boolean networks (BNs) has become an established method for analyzing intracellular communications over the last few decades. What’s more, BNs provide a course-grained approach, not only to understanding molecular communications, but also for targeting pathway components that alter the long-term outcomes of the system. This has come to be known as phenotype control theory. In this review we study the interplay of various approaches for controlling gene regulatory networks such as: algebraic methods, control kernel, feedback vertex set, and stable motifs. The study will also include comparative discussion between the methods, using an established cancer model of T-Cell Large Granular Lymphocyte Leukemia. Further, we explore possible options for making the control search more efficient using reduction and modularity. Finally, we will include challenges presented such as the complexity and the availability of software for implementing each of these control techniques. [ABSTRACT FROM AUTHOR]

Null models are crucial tools for investigating network topological structures. However, research on null models for higher-order networks is still relatively scarce. In this study, we introduce an innovative method to construct null models for hypergraphs, namely the hyperedge swapping-based method. By preserving certain network properties while altering others, we generate six hyper-null models with various orders and analyze their interrelationships. To validate our approach, we first employ hypergraph entropy to assess the randomness of these null models across four datasets. Furthermore, we examine the differences in important statistical properties between the various null models and the original networks. Lastly, we investigate the impact of hypergraph randomness on network dynamics using the proposed hyper-null models, focusing on dismantling and epidemic contagion. The findings show that our proposed hyper-null models are applicable to various scenarios. By introducing a comprehensive framework for generating and analyzing hyper-null models, this research opens up avenues for further exploration of the intricacies of network structures and their real-world implications. [ABSTRACT FROM AUTHOR]

Oscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not possible to know the exact connection weights. Therefore, it is important to determine the structural properties of a network necessary to generate oscillations. Here, we provide a proof that uses dynamical system theory to prove that an odd number of inhibitory nodes and strong enough connections are necessary to generate oscillations in a single cycle threshold-linear network. We illustrate these analytical results in a biologically plausible network with either firing-rate based or spiking neurons. Our work provides structural properties necessary to generate oscillations in a network. We use this knowledge to reconcile recent experimental findings about oscillations in basal ganglia with classical findings.

Alternative stable ecosystem states are possible under the same environmental conditions in models of two or three interacting species and an array of feedback loops. However, multispecies food webs might weaken the feedbacks loops that can create alternative stable states. To test how this potential depends on food web properties, we develop a many-species model where consumer Allee effects emerge from consumer-resource interactions. We evaluate the interactive effects of food web connectance, interspecific trait diversity, and two classes of feedbacks: specialized feedbacks, where consumption of individual resources declines at high resource abundance (e.g., from schooling or reaching size refugia), and aggregate feedbacks, where overall resource abundance reduces consumer recruitment (e.g., from resources enhancing competition or mortality experienced by recruits). We find that aggregate feedbacks maintain, and specialized feedbacks reduce, the potential for alternative states. Interspecific trait diversity decreases the prevalence of alternative stable states more for specialized than for aggregate feedbacks. Increasing food web connectance increases the potential for alternative stable states for aggregated feedbacks but decreases it for specialized feedbacks, where losing vulnerable consumers can cascade into food web collapses. Altogether, multispecies food webs can limit the set of processes that create alternative stable states and impede consumer recovery from disturbance. [ABSTRACT FROM AUTHOR]

In this study, we introduce the importance of elevated membrane potentials (MPs) in the prefrontal cortex (PFC) compared to that in the posterior parietal cortex (PPC), based on new observations of different MP levels in these areas. Through experimental data and spiking neural network modeling, we investigated a possible mechanism of the elevated membrane potential in the PFC and how these physiological differences affect neural network dynamics and cognitive functions in the PPC and PFC. Our findings indicate that NMDA receptors may be a main contributor to the elevated MP in the PFC region and highlight the potential of using a modeling toolkit to investigate the means by which changes in synaptic properties can affect neural dynamics and potentiate desirable cognitive functions through population activities in the corresponding brain regions. [ABSTRACT FROM AUTHOR]

In this study, we explore the relationship between inter-organizational network dynamics and innovation outcomes. We focus on node turnover and argue that both cluster and broker dynamics can range from low (stable) to high (volatile), resulting in differentiated outcomes. The data comprises 318 consortium members participating in 104 R&D consortia forged in a 23-year period in the Dutch water sector. Our analysis reveals two equifinal combinations (stable brokers – volatile clusters and volatile brokers – stable clusters) that both generate significantly higher innovation outcomes compared to networks with low, moderate, or high dynamics across the entire network. • The study addresses the relationship between network dynamics and innovation outcomes. • Network dynamics is analyzed in terms of node turnover. • Quantitative research on the evolution of inter-organizational networks. • A 23 year longitudinal data set of 318 consortium members in 104 R&D consortia. • Stable brokers and dynamic clusters or vice versa lead to the best outcomes. [ABSTRACT FROM AUTHOR]

Hippocampal area CA3 performs the critical auto-associative function underlying pattern completion in episodic memory. Without external inputs, the electrical activity of this neural circuit reflects the spontaneous spiking interplay among glutamatergic Pyramidal neurons and GABAergic interneurons. However, the network mechanisms underlying these resting-state firing patterns are poorly understood. Leveraging the Hippocampome.org knowledge base, we developed a data-driven, large-scale spiking neural network (SNN) model of mouse CA3 with 8 neuron types, 90,000 neurons, 51 neuron-type specific connections, and 250,000,000 synapses. We instantiated the SNN in the CARLsim4 multi-GPU simulation environment using the Izhikevich and Tsodyks-Markram formalisms for neuronal and synaptic dynamics, respectively. We analyzed the resultant population activity upon transient activation. The SNN settled into stable oscillations with a biologically plausible grand-average firing frequency, which was robust relative to a wide range of transient activation. The diverse firing patterns of individual neuron types were consistent with existing knowledge of cell type-specific activity in vivo. Altered network structures that lacked neuron- or connection-type specificity were neither stable nor robust, highlighting the importance of neuron type circuitry. Additionally, external inputs reflecting dentate mossy fibers shifted the observed rhythms to the gamma band. We freely released the CARLsim4-Hippocampome framework on GitHub to test hippocampal hypotheses. Our SNN may be useful to investigate the circuit mechanisms underlying the computational functions of CA3. Moreover, our approach can be scaled to the whole hippocampal formation, which may contribute to elucidating how the unique neuronal architecture of this system subserves its crucial cognitive roles. [ABSTRACT FROM AUTHOR]

The differential entropy of a continuous waveform is defined over the period spanned by the recording. The time-dependent information dynamics of the observed process are not accessible to this measure. We review here the construction and validation of approximate time-dependent measures of information dynamics. [ABSTRACT FROM AUTHOR]

Managers need to periodically evaluate any exchange partner to decide whether to continue or dissolve the exchange tie, but doing so can be challenging because of causal ambiguity: it can be difficult to attribute organizational performance to any specific underlying factor. One way managers may evaluate their exchange partners is by observing the performance trajectories of competitors who rely on the same exchange partners. We propose a theory of vicarious performance feedback and test it in the context of Formula One motor racing. We find that a firm building a Formula One racing car is more likely to end an exchange relationship with an engine supplier after that supplier's other customers experience an episode of poor performance relative to their historic track record. In line with an attention-based view of the firm, this behavior occurs when the firm's own performance is below its aspiration level. This work extends our understanding of how managers use vicarious learning to supplement their direct experience when evaluating their exchange partners, expands our thinking about network dynamics by showing how network neighbors' experiences can influence tie decisions made within a dyad, and contributes to the cognitive foundations of problemistic search by showing how external information is integrated into managers' responses to their own firm's underperformance. [ABSTRACT FROM AUTHOR]

The ability to faithfully represent real social networks is critical from the perspective of testing various what-if scenarios which are not feasible to be implemented in a real system as the system’s state would be irreversibly changed. High fidelity simulators allow one to investigate the consequences of different actions before introducing them to the real system. For example, in the context of social systems, an accurate social network simulator can be a powerful tool used to guide policy makers, help companies plan their advertising campaigns or authorities to analyse fake news spread. In this study we explore different Social Network Simulators (SNSs) and assess to what extent they are able to mimic the real social networks. We conduct a critical review and assessment of existing Social Network Simulators under the Digital Twin-Oriented Modelling framework proposed in our previous study. We subsequently extend one of the most promising simulators from the evaluated ones, to facilitate generation of social networks of varied structural complexity levels. This extension brings us one step closer to a Digital Twin Oriented SNS (DT Oriented SNS). We also propose an approach to assess the similarity between real and simulated networks with the composite performance indexes based on both global and local structural measures, while taking runtime of the simulator as an indicator of its efficiency. We illustrate various characteristics of the proposed DT Oriented SNS using a well known Karate Club network as an example. While not considered to be of sufficient complexity, the simulator is intended as one of the first steps on a journey towards building a Digital Twin of a social network that perfectly mimics the reality.

Knowing which nodes are influential in a complex network and whether the network can be influenced by a small subset of nodes is a key part of network analysis. However, many traditional measures of importance focus on node level information without considering the global network architecture. We use the method of trophic analysis to study directed networks and show that both ‘influence’ and ‘influenceability’ in directed networks depend on the hierarchical structure and the global directionality, as measured by the trophic levels and trophic coherence, respectively. We show that in directed networks trophic hierarchy can explain: the nodes that can reach the most others; where the eigenvector centrality localizes; which nodes shape the behaviour in opinion or oscillator dynamics; and which strategies will be successful in generalized rock–paper–scissors games. We show, moreover, that these phenomena are mediated by the global directionality. We also highlight other structural properties of real networks related to influenceability, such as the pseudospectra, which depend on trophic coherence. These results apply to any directed network and the principles highlighted—that node hierarchy is essential for understanding network influence, mediated by global directionality—are applicable to many real-world dynamics.

A large body of evidence suggests that brain signal complexity (BSC) may be an important indicator of healthy brain functioning or alternately, a harbinger of disease and dysfunction. However, despite recent progress our current understanding of how BSC emerges and evolves in large-scale networks, and the factors that shape these dynamics, remains limited. Here, we utilized resting-state functional near-infrared spectroscopy (rs-fNIRS) to capture and characterize the nature and time course of BSC dynamics within large-scale functional networks in 107 healthy participants ranging from 6-13 years of age. Age-dependent increases in spontaneous BSC were observed predominantly in higher-order association areas including the default mode (DMN) and attentional (ATN) networks. Our results also revealed asymmetrical developmental patterns in BSC that were specific to the dorsal and ventral ATN networks, with the former showing a left-lateralized and the latter demonstrating a right-lateralized increase in BSC. These age-dependent laterality shifts appeared to be more pronounced in females compared to males. Lastly, using a machine-learning model, we showed that BSC is a reliable predictor of chronological age. Higher-order association networks such as the DMN and dorsal ATN demonstrated the most robust prognostic power for predicting ages of previously unseen individuals. Taken together, our findings offer new insights into the spatiotemporal patterns of BSC dynamics in large-scale intrinsic networks that evolve over the course of childhood and adolescence, suggesting that a network-based measure of BSC represents a promising approach for tracking normative brain development and may potentially aid in the early detection of atypical developmental trajectories.

In this paper, we describe the orbital structure of homogeneous fuzzy parallel dynamical systems on Zadeh operators. In particular, we show that, when the (global) evolution operator is either the Zadeh conjunction operator AND or the Zadeh disjunction operator OR, all the points of the system are fixed points or eventually fixed points. Specifically, such systems present infinitely many fixed points. On the other hand, when the (global) evolution operator is either the Zadeh operator NOR or the Zadeh operator NAND, the orbital structure consists of an only fixed point and infinitely many 2‐periodic points. [ABSTRACT FROM AUTHOR]

Scholars and journalistic accounts have devoted growing interest to the centralizing trends characterizing platforms and the "platform society." They often oppose this model to the alleged openness, horizontality, and "equality" they attach to "networks." Such depictions seem willing to give up on a thorough consideration of network structure, which appears nowadays less fundamental to reflect on the digital world (and society) than it was 25 years ago. Or even question whether the network society model, as proposed by Castells, is unsuitable for describing contemporary society. In our opinion, the dichotomy opposing the (alleged) openness and egalitarian nature of networks (and of the network society), to the current centralization trends characterizing the platform society, as well as the subsequent assumption that networks are an outdated heuristic tool, derive from a misunderstanding of networks' structure and dynamics. Scholars have shown that the structure of most complex networks can be defined as "scale-free," following a power-law distribution. Complex networks, indeed, show the tendency for some nodes to become more interconnected than others (thus becoming "hubs"). In this, the understanding of network structure proposed in Castells (1996), contrary to the rhetoric considering networks intrinsically as "egalitarian," is still a conceptual and analytical tool of the utmost importance for understanding the so-called "platform society." This paper focuses on networks, network models, and the network society, reviewing what was proposed in Castells (1996). We argue that the social and platform ecosystem we are witnessing today can be understood from the perspective of scale-free networks and is, indeed, consistent with the premises provided in 1996. When observing networks, we address both structure and agency, and both the macro (network morphology) and micro (networked individualism and sociability) levels [ABSTRACT FROM AUTHOR]

Inferring the topology of a network from network dynamics is a significant problem with both theoretical research significance and practical value. This paper considers how to reconstruct the network topology according to the continuous-time data on the network. Inspired by the generative adversarial network(GAN), we design a deep learning framework based on network continuous-time data. The framework predicts the edge connection probability between network nodes by learning the correlation between network node state vectors. To verify the accuracy and adaptability of our method, we conducted extensive experiments on scale-free networks and small-world networks at different network scales using three different dynamics: heat diffusion dynamics, mutualistic interaction dynamics, and gene regulation dynamics. Experimental results show that our method significantly outperforms the other five traditional correlation indices, which demonstrates that our method can reconstruct the topology of different scale networks well under different network dynamics. [ABSTRACT FROM AUTHOR]

With the advent of volumetric EM techniques, large connectomic datasets are being created, providing neuroscience researchers with knowledge about the full connectivity of neural circuits under study. This allows for numerical simulation of detailed, biophysical models of each neuron participating in the circuit. However, these models typically include a large number of parameters, and insight into which of these are essential for circuit function is not readily obtained. Here, we review two mathematical strategies for gaining insight into connectomics data: linear dynamical systems analysis and matrix reordering techniques. Such analytical treatment can allow us to make predictions about time constants of information processing and functional subunits in large networks. [ABSTRACT FROM AUTHOR]

Traffic congestion on a road segment typically begins as a small-scale spatiotemporal event that can then propagate throughout a road network and produce large-scale disruptions to a transportation system. In current techniques for the analysis of network flow, data is often aggregated to relatively large (e.g. 5 min) discrete time steps that obscure the small-scale spatiotemporal interactions that drive larger-scale dynamics. We propose a new method that handles fine-grained data to better capture those dynamics. Propagation patterns of traffic congestion are represented as spatiotemporally connected events. Each event is captured as a time series at the temporal resolution of the available trajectory data and at the spatial resolution of the network edge. The spatiotemporal propagation patterns of traffic congestion are captured using Dynamic Time Warping and represented as a set of directed acyclic graphs of spatiotemporal events. Results from this method are compared to an existing method using fine-grained data derived from an agent-based model of traffic simulation. Our method outperforms the existing method. Our method also successfully detects congestion propagation patterns that were reported by media news using sparse real-world data derived from taxis. [ABSTRACT FROM AUTHOR]

Increasing research highlights heterogeneity in patterns of social network change, with growing evidence that these patterns are shaped in part by social structure. The role of social and structural neighborhood conditions in the addition and loss of kin and non-kin network members, however, has not been fully considered. In this paper, we argue that the residential neighborhood context can either facilitate or prevent the turnover of core network relationships in later life – a period of the life course characterized by heightened reliance on network ties and vulnerability to neighborhood conditions. Using longitudinal data from the National Social Life, Health, and Aging Project linked with data from the American Community Survey, we find that higher levels of neighborhood concentrated disadvantage are associated with the loss of older adults' kin and non-kin network members over time. Higher levels of perceived neighborhood social interaction, however, are associated with higher rates of adding non-kin network members and lower rates of adding kin network members over time. We suggest that neighborhood conditions, including older adults' perceptions of neighborhood social life, represent an underexplored influence on kin and non-kin social network dynamics, which could have implications for access to social resources later in the life course. • Neighborhood conditions influence the addition and loss of older adults' personal network members over time. • These associations emerge even when accounting for life-course transitions, health, and social position. • Older adults living in higher levels of local concentrated disadvantage lose kin and non-kin network ties at higher rates. • Greater perceived neighborhood social ties are associated with higher rates of non-kin additions and lower rates of kin additions. [ABSTRACT FROM AUTHOR]

In this paper we propose a simple approach for modelling the functioning of a cluster of interconnected infiltration basins: such a modelling is crucial for correctly managing artificial aquifer recharge. We model and dynamically quantify the flow from the bottom of each basin, towards the outer walls of the cluster, and between adjacent basins: as a result of this modelling, saturated hydraulic conductivity parameters are estimated. This goal is accomplished simply by using mass balance arguments and Darcy's law, considering only water levels data and the total water inflow daily bestowed to the trenches; by using a declipping technique, automated recorded data are suitably filtered; then, from these data, an efficient algorithm is implemented for determining which basin is instantaneously fed. This approach can be used for managing the efficiency and performance of such facilities over time, and it can be easily generalized. The model is tested over a real‐life site in Castellana Grotte (Southeastern Italy), where an infiltration basins cluster is used as final receptor of the effluents from a tertiary treatment wastewater plant. Novelty and International Appeal Statement: We propose a simple approach for modelling the functioning of a cluster of interconnected infiltration basins. By a dynamical systems approach, we model the flow from the bottom of each basin, towards outer walls of the cluster, and between adjacent basins: as a result, saturated hydraulic conductivity parameters are estimated. [ABSTRACT FROM AUTHOR]

Developmental dyslexia is characterized by a deficit of phonological awareness whose origin is related to atypical neural processing of speech streams. This can lead to differences in the neural networks that encode audio information for dyslexics. In this work, we investigate whether such differences exist using functional near-infrared spectroscopy (fNIRS) and complex network analysis. We have explored functional brain networks derived from low-level auditory processing of nonspeech stimuli related to speech units such as stress, syllables or phonemes of skilled and dyslexic seven-year-old readers. A complex network analysis was performed to examine the properties of functional brain networks and their temporal evolution. We characterized aspects of brain connectivity such as functional segregation, functional integration or small-worldness. These properties are used as features to extract differential patterns in controls and dyslexic subjects. The results corroborate the presence of discrepancies in the topological organizations of functional brain networks and their dynamics that differentiate between control and dyslexic subjects, reaching an Area Under ROC Curve (AUC) up to 0.89 in classification experiments. [ABSTRACT FROM AUTHOR]