Narrow Results

Cryptocurrencies have emerged as a new asset class. In order to provide a thorough understanding of this new asset class, we study the dependencies in tail risk events within cryptocurrencies, and provide a hedging alternative in this paper. First, we adopt the Financial Risk Meter approach for cryptocurrencies, which is able to identify individual risk characteristics and indicate systemic risk in a network topology. Next, we detect the interdependencies across digital coins and study the spillover effects. Finally, we construct tail event sensitive portfolios and test the performance versus traditional approaches from January 2019 to May 2022.

Recently, numerous attempts have been made to understand the dynamic behavior of complex brain systems using neural network models. The fluctuations in blood-oxygen-level-dependent (BOLD) brain signals at less than 0.1 Hz have been observed by functional magnetic resonance imaging (fMRI) for subjects in a resting state. This phenomenon is referred to as a "default-mode brain network." In this study, we model the default-mode brain network by functionally connecting neural communities composed of spiking neurons in a complex network. Through computational simulations of the model, including transmission delays and complex connectivity, the network dynamics of the neural system and its behavior are discussed. The results show that the power spectrum of the modeled fluctuations in the neuron firing patterns is consistent with the default-mode brain network's BOLD signals when transmission delays, a characteristic property of the brain, have finite values in a given range.

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.

Air transportation networks play important roles in human mobility. In this paper, from the perspective of multilayer network mechanism, the dynamics of the Chinese air transportation network are extensively investigated. A multilayer-based passengers re-scheduling model is introduced, and a multilayer cooperation (MC) approach is proposed to improve the efficiency of network traffic under random failures. We use two metrics: the success rate and the extra transfer number, to evaluate the efficiency of re-scheduling. It is found that a higher success rate of passengers re-scheduling can be obtained by MC, and MC is stronger for resisting the instability of the capacity of links. Furthermore, the explosion of the number of extra transfer can be well restrained by MC. Our work will highlight a better understanding of the dynamics and robustness of the Chinese air transportation network.

Our numerical simulation first displays exponential increase of the mean number of attractors with N for K = 2,3,4 and 50≦ N ≦350. The mean length of attractors also increases exponentially with N for K = 2, but increases linearly with N for K = 3,4. We further found the larger the K the larger S/N value; which yields the results that the mean length and the mean number of attractors of critical random Boolean networks will decrease with larger K.

Networks have been studied mainly by statistical methods which emphasize their topological structure. Here, one collects some mathematical tools and results which might be useful to study both the dynamics of agents living on the network and the networks themselves as evolving dynamical systems. They include decomposition of differential dynamics, ergodic techniques, estimates of invariant measures, construction of non deterministic automata, logical approaches, etc. A few network examples are discussed as an application of the dynamical tools.

This paper continues the study of patterns of synchrony (equivalently, balanced colorings or flow-invariant subspaces) in symmetric coupled cell networks, and their relation to fixed-point spaces of subgroups of the symmetry group. Let Γ be a permutation group acting on the set of cells. We define the group network , whose architecture is entirely determined by the group orbits of Γ. We prove that if Γ has the "balanced extension property" then every balanced coloring of is a fixed-point coloring relative to the automorphism group of the group network. This theorem applies in particular when Γ is cyclic or dihedral, acting on cells as the symmetries of a regular polygon, and in these cases the automorphism group is Γ itself. In general, however, the automorphism group may be larger than Γ. Several examples of this phenomenon are discussed, including the finite simple group of order 168 in its permutation representation of degree 7. More dramatically, for some choices of Γ there exist balanced colorings of that are not fixed-point colorings. For example, there exists an exotic balanced 2-coloring when Γ is the symmetry group of the two-dimensional square lattice. This coloring is doubly periodic, and its reduction modulo 8 leads to a finite group with similar properties. Although these patterns do not arise from fixed-point spaces, we provide a group-theoretic explanation of their balance property in terms of a sublattice of index two.

This paper deals with the modeling of production processes in automotive industries by models based on partial differential equations. The basic idea consists on the derivation of kinetic equations to model production flow on an assembly line. Numerical results based on data of an assembly plant are presented. The work implements a recent discussion for general flow on unstructured networks.

We are interested in flows on general networks and derive a kinetic equation describing general production, social or transportation networks. Corresponding macroscopic transport equations for large time and homogenized behavior are obtained and studied numerically. This work continues a recent discussion [Averaged kinetic models for flows on unstructured networks, Kinetic Related Models 4 (2011) 1081–1096] and provides additionally explicit equilibrium solutions, second-order macroscopic approximations as well as numerical simulations on a large scale homogenized network.

The evolution of unconditional cooperation is one of the fundamental problems in science. A new solution is proposed to solve this puzzle. We treat this issue with an evolutionary model in which agents play the Prisoner's Dilemma on signed networks. The topology is allowed to co-evolve with relational signs as well as with agent strategies. We introduce a strategy that is conditional on the emotional content embedded in network signs. We show that this strategy acts as a catalyst and creates favorable conditions for the spread of unconditional cooperation. In line with the literature, we found evidence that the evolution of cooperation most likely occurs in networks with relatively high chances of rewiring and with low likelihood of strategy adoption. While a low likelihood of rewiring enhances cooperation, a very high likelihood seems to limit its diffusion. Furthermore, unlike in nonsigned networks, cooperation becomes more prevalent in denser topologies.

Network science has experienced unprecedented rapid development in the past two decades. The network perspective has also been widely applied to explore various complex systems in great depth. In the first decade, fundamental characteristics of complex network structure, such as the small-worldness, scale-freeness, and modularity, of various complex networked systems were harvested from analyzing big empirical data. The associated dynamical processes on complex networks were also heavily studied. In the second decade, more attention was devoted to investigating the control of complex networked systems, ranging from fundamental theories to practical applications. Here we briefly review the recent progress regarding network dynamics and control, mainly concentrating on research questions proposed in the six papers we collected for this topical issue. This review closes with possible research directions along this line, and several important problems to be solved. We expect that, in the near future, network control will play an even bigger role in more fields, helping us understand and control many complex natural and engineered systems.

Comprehensively understanding the dynamics of biological systems is one of the greatest challenges in biology. Vastly improved biological technologies have provided vast amounts of information that must be understood by bioinformatics and systems biology researchers. Gene regulations have been frequently modeled by ordinary differential equations or graphical models based on time-course gene expression profiles. The state-of-the-art computational approaches for analyzing gene regulations assume that their models are same throughout time-course experiments. However, these approaches cannot easily analyze transient changes at a time point, such as diauxic shift. We propose a score that analyzes the gene regulations at each time point. The score is based on the information gains of information criterion values. The method detects the shifts in gene regulatory networks (GRNs) during time-course experiments with single-time-point resolution. The effectiveness of the method is evaluated on the diauxic shift from glucose to lactose in Escherichia coli. Gene regulation shifts were detected at two time points: the first corresponding to the time at which the growth of E. coli ceased and the second corresponding to the end of the experiment, when the nutrient sources (glucose and lactose) had become exhausted. According to these results, the proposed score and method can appropriately detect the time of gene regulation shifts. The method based on the proposed score provides a new tool for analyzing dynamic biological systems. Because the score value indicates the strength of gene regulation at each time point in a gene expression profile, it can potentially infer hidden GRNs from time-course experiments.

Entrepreneurs’ networks are important for resource acquisition during early venturing. Interestingly, resource needs rapidly change during early venture development, which may necessitate a change in entrepreneurs’ networks. This study examines how the size of an early-stage entrepreneur’s network affects the subsequent structural (e.g., size) and content (e.g., knowledge diversity) character of their network. Additionally, how the strength of an early-stage entrepreneur’s network ties affects the subsequent structural (e.g., size and tie strength) and content (e.g., knowledge diversity) character of their network is explored. Data from the PSED-I were used to analyze the relationships. Results suggest the relationship between network size at time one and network size at time two is positive. Results further suggest a negative relationship between the number of weak ties at time one and the number of strong ties at time two. In addition, a positive relationship was found between network size at time one and network knowledge diversity at time two. Overall, this study contributes by providing a replication of results from recent work examining dynamics of entrepreneurs’ networks and it extends research on the topic by testing proposals utilizing panel data and through integrating novel theoretical perspectives.