Community Detection Based on Graph Representation Learning in Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

Filogenias: conceptos y generalidades

Any research in biology is an exercise of comparison that includes the study of evolution. The investigation of evolutionary patterns in either of its two approaches (micro or macroevolutionary) raises methodological challenges for any researcher interested in these topics. These approaches have a common interest in understanding the origin the relationships between the studied organisms, although the temporal scales and the level of organization in which they focus are different. Currently, phylogenies are the most robust tool to explain ancestor-descendant relationships between a set of organisms. These diagrams, which are two-dimensional (cladograms) or multidimensional (networks), can be estimated with different approximations (maximum parsimony, máximum likelihood, and bayesian inference) according to the data available and the purpose of the investigation. This review presents an introduction to the methods available for the construction of phylogenies, including the traditional perspective that uses diagrams based on dichotomies and the new trends that try to visualize more complex patterns through evolutionary networks.

A multilevel hypernetworks approach to assess coordination and communication in player interactions in sports teams as co-evolutionary networks

BACKGROUND: This paper presents an introduction and brief appraisal of the use of hyper networks metrics and its potential practical application in examining team dynamics' coordination patterns collective sports. AIM: Throughout their critique piece, we highlighted that game analysis, including the hyper network concept, may help overcome the limitations of previous tools such as social network measures. FINDINGS AND CONCLUSIONS: While the social network analysis generally considers only dyadic interactions (e.g., between two players), the hyper networks also take into account a multidimensional perspective, including both player level and team level communication and coordination. We also evidenced that new studies using hyper network metrics are required in a range of team sports, mainly using data gathered from official competition matches.

Volatility Modeling for Currency Pairs and Stock Indices by Means of Complex Networks

Financial markets are complex systems. Network analysis is an innovative method for improving data sharing and knowledge discovery in financial data. Oriented weighted networks were created for the Shanghai Composite, S&P500, DAX30, CAC40, Nikkei225, FTSE100, IBEX35 indexes, for CNY-JPY, EUR-USD, GBP-EUR, RUB-CNY and for cryptocurrency BTC-USD. We considered data since January 6, 2006 to September 6, 2019. The complex networks had a similar structure for both types of markets, which was divided into the central part (core) and the outer one (loops). The emergence of such a structure reflects the fact that, for the most part, the stock and currency markets develop around some significant state of volatility, but occasionally anomalies occur when the states of volatility deviate from the core. Comparing the topology of evolutionary networks and the differences found for the stock and currency markets networks, we can conclude that stock markets are characterized by a greater variety of volatility patterns than currency ones. At the same time, the cryptocurrency market network showed a special mechanism of volatility evolution compared to the currency and stock market networks.

Deep-Reinforcement Learning-Based Co-Evolution in a Predator–Prey System

Understanding or estimating the co-evolution processes is critical in ecology, but very challenging. Traditional methods are difficult to deal with the complex processes of evolution and to predict their consequences on nature. In this paper, we use the deep-reinforcement learning algorithms to endow the organism with learning ability, and simulate their evolution process by using the Monte Carlo simulation algorithm in a large-scale ecosystem. The combination of the two algorithms allows organisms to use experiences to determine their behavior through interaction with that environment, and to pass on experience to their offspring. Our research showed that the predators’ reinforcement learning ability contributed to the stability of the ecosystem and helped predators obtain a more reasonable behavior pattern of coexistence with its prey. The reinforcement learning effect of prey on its own population was not as good as that of predators and increased the risk of extinction of predators. The inconsistent learning periods and speed of prey and predators aggravated that risk. The co-evolution of the two species had resulted in fewer numbers of their populations due to their potentially antagonistic evolutionary networks. If the learnable predators and prey invade an ecosystem at the same time, prey had an advantage. Thus, the proposed model illustrates the influence of learning mechanism on a predator–prey ecosystem and demonstrates the feasibility of predicting the behavior evolution in a predator–prey ecosystem using AI approaches.

Emergence of Network Bifurcation Triggered by Entanglement

In many non-linear systems, such as plasma oscillation, boson condensation, chemical reaction, and even predatory-prey oscillation, the coarse-grained dynamics are governed by an equation containing anti-symmetric transitions, known as the anti-symmetric Lotka-Volterra (ALV) equations. In this work, we prove the existence of a novel bifurcation mechanism for the ALV equations, where the equilibrium state can be drastically changed by flipping the stability of a pair of fixed points. As an application, we focus on the implications of the bifurcation mechanism for evolutionary networks; we found that the bifurcation point can be determined quantitatively by the microscopic quantum entanglement. The equilibrium state can be critically changed from one type of global demographic condensation to another state that supports global cooperation for homogeneous networks. In other words, our results indicate that there exist a class of many-body systems where the macroscopic properties are invariant with a certain amount of microscopic entanglement, but they can be changed abruptly once the entanglement exceeds a critical value. Furthermore, we provide numerical evidence showing that the emergence of bifurcation is robust against the change of the network topologies, and the critical values are in good agreement with our theoretical prediction. These results show that the bifurcation mechanism could be ubiquitous in many physical systems, in addition to evolutionary networks.