arXiv subjects

Jin-Li Guo

Publications and source records attributed to Jin-Li Guo.

18 recordsLinked to original sources

arXiv2024

Competitive and Weighted Evolving Simplicial Complexes

A simplex-based network is referred to as a higher-order network, in which describe that the interactions can include more than two nodes. Many multicomponent interactions can be grasped through simplicial complexes, which have recently found applications in social, technological, and biological contexts. The paper first proposes a competitive evolving model of higher-order networks. We introduce the difference equation analysis approach in the high-order network to make the analysis network more rigorous. It avoids the assumption that the degrees of nodes are continuous in the traditional analysis network. We obtain an analytical expression for the distribution of higher-order degrees by employing the theory of Poisson processes. The established results indicate that in a d-order network the scale-free behavior for the (d-1)-dim simplex with respect to the d-order degree is controlled by the competitiveness factor. As the competitiveness increases, the d-order degree of the (d-1)-dim simplex is bent under the logarithmic coordinates. While the e(<d-1)-dim simplex with respect to the d-order degree exhibits scale-free behavior. Second, by considering the weight changes of the neighboring simplices, as triggered by the selected simplex, a new weighted evolving model in higher-order networks is proposed. The results of the competitive evolving model of higher-order networks are used to analyze the weighted evolving model so that obtained are the analytical expressions of the higher-order degree distribution and higher-order strength density function of weighted higher-order networks. The outcomes of the simulation experiments are consistent with the theoretical analysis.

physics.soc-ph↗

arXiv2023

Emergence of Bending Power Law in Higher-Order Networks

In the past two decades, a series of important results have been established in the empirical and theoretical modeling of complex networks, although considered are mainly pairwise networks. However, with the development of science and technology, an increasing number of higher-order networks with many-body interactions have gradually moved to the center stage of research when real-life systems are investigated. In the paper, the concept of higher-order degree is introduced to higher-order networks, and a bending power law (BPL) model with continuous-time growth is proposed. The evolution mechanism and topological properties of the general higher-order network are studied. The batch effect of low dimensional simplex is considered. The model is analyzed by using the mean-field method and Poisson process theory. The stationary average higher-order degree distribution of simplices is expressed analytically. The obtained analytical results agree well with those observed through simulations. In particular, this paper shows that the higher-order degree distribution of simplices in the network processes a property of bending power law, and the scale-free property of the higher-order degree is controlled by the higher-order edge, the simplex dimension and the feature parameter of the model. The BPL model of higher-order networks not only generalizes the NGF model, but also the famous scale-free model of complex networks to higher-order networks.

physics.soc-ph↗

arXiv2016

The Evolution of Hyperedge Cardinalities and Bose-Einstein Condensation in Hypernetworks

To depict the complex relationship among nodes and the evolving process of a complex system, a Bose-Einstein hypernetwork is proposed in this paper. Based on two basic evolutionary mechanisms, growth and preference jumping, the distribution of hyperedge cardinalities is studied. The Poisson process theory is used to describe the arrival process of new node batches. And, by using the Poisson process theory and a continuity technique, the hypernetwork is analyzed and the characteristic equation of hyperedge cardinalities is obtained. Additionally, an analytical expression for the stationary average hyperedge cardinality distribution is derived by employing the characteristic equation, from which Bose-Einstein condensation in the hypernetwork is obtained. The theoretical analyses in this paper agree with the conducted numerical simulations. This is the first study on the hyperedge cardinality in hypernetworks, where Bose-Einstein condensation can be regarded as a special case of hypernetworks. Moreover, a condensation degree is also discussed with which Bose-Einstein condensation can be classified.

physics.soc-ph↗

arXiv2015

Weighted Hypernetworks

Complex network theory has been used to study complex systems. However, many real-life systems involve multiple kinds of objects . They can't be described by simple graphs. In order to provide complete information of these systems, we extend the concept of evolving models of complex networks to hypernetworks. In this work, we firstly propose a non-uniform hypernetwork model with attractiveness, and obtain the stationary average hyperdegree distribution of the non-uniform hypernetwork. Furthermore, we develop a model for weighted hypernetworks that couples the establishment of new hyperedges and nodes and the weights' dynamical evolution. We obtain the stationary average hyperdegree and hyperstrength distribution by using the hyperdegree distribution of the hypernetwork model with attractiveness, respectively. In particular, the model yields a nontrivial time evolution of nodes' properties and scale-free behavior for the hyperdegree and hyperstrength distribution. It is expected that our work may give help to the study of the hypernetworks in real-world systems.

physics.soc-ph↗

arXiv2015

Evolving hypernetwork model based on WeChat user relations

Based on the theory of hypernetwork and WeChat online social relations, the paper proposes an evolving hypernetwork model with the competitiveness and the age of nodes. In the model, nodes arrive at the system in accordance with Poisson process and are gradual aging. We analyze the model by using a Poisson process theory and a continuous technique, and give a characteristic equation of hyperdegrees. We obtain the stationary average hyperdegree distribution of the hypernetwork by the characteristic equation. The numerical simulations of the models agree with the analytical results well. It is expected that our work may give help to the study of WeChat information transmission dynamics and mobile e-commerce.

cs.SI↗

arXiv2015

Upper Entropy Axioms and Lower Entropy Axioms

The paper suggests the concepts of an upper entropy and a lower entropy. We propose a new axiomatic definition, namely, upper entropy axioms, inspired by axioms of metric spaces, and also formulate lower entropy axioms. We also develop weak upper entropy axioms and weak lower entropy axioms. Their conditions are weaker than those of Shannon-Khinchin axioms and Tsallis axioms, while these conditions are stronger than those of the axiomatics based on the first three Shannon-Khinchin axioms for superstatiscs. The expansibility, subadditivity and strong subadditivity of entropy are obtained in the new axiomatics. Tsallis statistics is a special case of the superstatistics which satisfies our axioms. Moreover, different forms of information measures, such as Shannon entropy, Daroczy entropy, Tsallis entropy and other entropies, can be unified under the same axiomatic framework.

math.PR↗

arXiv2015

Brand effect versus competitiveness in hypernetworks

A few of evolving models in hypernetworks have been proposed based on uniform growth. In order to better depict the growth mechanism and competitive aspect of real hypernetworks, we propose a model in term of the non-uniform growth. Besides hyperdegrees, the other two important factors are introduced to underlie preferential attachment. One dimension is the brand effect and the other is the competitiveness. Our model can accurately describe the evolution of real hypernetworks. The paper analyzes the model and calculates the stationary average hyperdegree distribution of the hypernetwork by using Poisson process theory and a continuous technique. We also address the limit in which this model has a condensation. The theoretical analyses agree with numerical simulations. Our model is universal, in that the standard preferential attachment, the fitness model in complex networks and scale-free model in hypernetworks can all be seen as degenerate cases of the model.

physics.soc-ph↗

arXiv2014

Zero-determinant strategies in iterated multi-strategy games

Self-serving, rational agents sometimes cooperate to their mutual benefit. The two-player iterated prisoner's dilemma game is a model for including the emergence of cooperation. It is generally believed that there is no simple ultimatum strategy which a player can control the return of the other participants. The recent discovery of the powerful class of zero-determinant strategies in the iterated prisoner's dilemma dramatically expands our understanding of the classic game by uncovering strategies that provide a unilateral advantage to sentient players pitted against unwitting opponents. However, strategies in the prisoner's dilemma game are only two strategies. Are there these results for general multi-strategy games? To address this question, the paper develops a theory for zero-determinant strategies for multi-strategy games, with any number of strategies. The analytical results exhibit a similar yet different scenario to the case of two-strategy games. Zero-determinant strategies in iterated prisoner's dilemma can be seen as degenerate case of our results. The results are also applied to the snowdrift game, the hawk-dove game and the chicken game.

cs.GT↗

arXiv2014

The co-evolution of brand effect and competitiveness in evolving networks

The principle that 'the brand effect is attractive' underlies preferential attachment. Here we show that the brand effect is just one dimension of attractiveness. Another dimension is competitiveness. We firstly develop a general framework that allows us to investigate the competitive aspect of real networks, instead of simply preferring popular nodes. Our model accurately describes the evolution of social and technological networks. The phenomenon which more competitive nodes become richer links can help us to understand the evolution of many competitive systems in nature and society. In general, the paper provides an explicit analytical expression of degree distributions of the network. In particular, the model yields a nontrivial time evolution of nodes' properties and scale-free behavior with exponents depending on the microscopic parameters characterizing the competition rules. Secondly, through theoretical analysis and numerical simulations, it reveals that our model has not only the universality for the homogeneous weighted network, but also the character for the heterogeneous weighted network. Thirdly, the paper also develops a model based on a profit-driven mechanism. It can better describe the observed phenomenon in enterprise cooperation networks. We show that standard preferential attachment, the growing random graph, the initial attractiveness model, the fitness model and weighted networks, can all be seen as degenerate cases of our model.

physics.soc-ph↗

arXiv2014

Comment on "Quantifying Long-term Scientific Impact"

The paper comments on "Quantifying long-term scientific impact". It indicates that there is a mistake of [D. S. Wang , C. Song, A. L. Barabasi, Quantifying long-term scientific impact, Science 342, 127 (2013), arXiv:1306.3293].

cs.DL↗

arXiv2013

Universality of competitive networks for weighted networks

In this paper, we propose a new model that allows us to investigate this competitive aspect of real networks in quantitative terms. Through theoretical analysis and numerical simulations, we find that the competitive network have the universality for a weighted network. The relation between parameters in the weighted network and the competitiveness in the competitive network is obtained. So we can use the expression of the degree distribution of the competitive model to calculate that and the strength of the weighted network directly. The analytical solution reveals that the degree distribution of the weighted network is correlated with the increment and initial value of edge weights, which is verified by numerical simulations. Moreover, the evolving pattern of a clustering coefficient along with network parameters such as the size of a network, an updating coefficient, an initial weight and the competitiveness are obtained by further simulations. Specially, it is necessary to point out that the initial weight plays equally significant role as updating coefficient in influencing the topological characteristics of the network.

physics.soc-ph↗

arXiv2011

Research on the visitor flow pattern of Expo 2010

Expo 2010 Shanghai China was a successful, splendid and unforgettable event, remaining us with valuable experiences. The visitor flow pattern of Expo is investigated in this paper. The Hurst exponent, mean value and standard deviation of visitor volume prove that the visitor flow is fractal with long-term stability and correlation as well as obvious fluctuation in short period. Then the time series of visitor volume is converted to complex network by visibility algorithm. It can be inferred from the topological properties of the visibility graph that the network is scale-free, small-world and hierarchically constructed, conforming that the time series are fractal and close relationship exit between the visitor volume on different days. Furthermore, it is inevitable to show some extreme visitor volume in the original visitor flow, and these extreme points may appear in group to a great extent.

physics.soc-ph↗

arXiv2010

Fractal Analysis on Human Behaviors Dynamics

The study of human dynamics has attracted much interest from many fields recently. In this paper, the fractal characteristic of human behaviors is investigated from the perspective of time series constructed with the amount of library loans. The Hurst exponents and length of non-periodic cycles calculated through Rescaled Range Analysis indicate that the time series of human behaviors is fractal with long-range correlation. Then the time series are converted to complex networks by visibility graph algorithm. The topological properties of the networks, such as scale-free property, small-world effect and hierarchical structure imply that close relationships exist between the amounts of repetitious actions performed by people during certain periods of time, especially for some important days. Finally, the networks obtained are verified to be not fractal and self-similar using box-counting method. Our work implies the intrinsic regularity shown in human collective repetitious behaviors.

physics.soc-ph↗

arXiv2010

Individual and Group Dynamics in Purchasing Activity

As a major part of the daily operation in an enterprise, purchasing frequency is of constant change. Recent approaches on the human dynamics can provide some new insights into the economic behaviors of companies in the supply chain. This paper captures the attributes of creation times of purchase orders to an individual vendor, as well as to all vendors, and further investigates whether they have some kind of dynamics by applying logarithmic binning to the construction of distribution plot. It's found that the former displays a power-law distribution with approximate exponent 2.0, while the latter is fitted by a mixture distribution with both power-law and exponential characteristics. Obviously, two distinctive characteristics are presented for the interval time distribution from the perspective of individual dynamics and group dynamics. Actually, this mixing feature can be attributed to the fitting deviations as they are negligible for individual dynamics, but those of different vendors are cumulated and then lead to an exponential factor for group dynamics. To better describe the mechanism generating the heterogeneity of purchase order assignment process from the objective company to all its vendors, a model driven by product life cycle is introduced, and then the analytical distribution and the simulation result are obtained, which are in good line with the empirical data.

physics.soc-ph↗

arXiv2010

S-curve networks and an approximate method for estimating degree distributions of complex networks

In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (Logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference value for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions (i.e., Barabási-Albert method) is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and use this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabási-Albert method commonly used in current network research.

physics.comp-ph↗

arXiv2010

Empirical study and modeling of human behaviour dynamics of comments on Blog posts

On-line communities offer a great opportunity to investigate human dynamics, because much information about individuals is registered in databases. In this paper, based on data statistics of online comments on Blog posts, we first present an empirical study of a comment arrival-time interval distribution. We find that people interested in some subjects gradually disappear and the interval distribution is a power law. According to this feature, we propose a model with gradually decaying interest. We give a rigorous analysis on the model by non-homogeneous Poisson processes and obtain an analytic expression of the interval distribution. Our analysis indicates that the time interval between two consecutive events follows the power-law distribution with a tunable exponent, which can be controlled by the model parameters and is in interval (1,+{\infty}). The analytical result agrees with the empirical results well, obeying an approximately power-law form. Our model provides a theoretical basis for human behaviour dynamics of comments on Blog posts.

cs.SI↗

arXiv2010

Weblog patterns and human dynamics with decreasing interest

Weblog is the fourth way of network exchange after Email, BBS and MSN. Most bloggers begin to write blogs with great interest, and then their interests gradually achieve a balance with the passage of time. In order to describe the phenomenon that people's interest in something gradually decreases until it reaches a balance, we first propose the model that describes the attenuation of interest and reflects the fact that people's interest becomes more stable after a long time. We give a rigorous analysis on this model by non-homogeneous Poisson processes. Our analysis indicates that the interval distribution of arrival-time is a mixed distribution with exponential and power-law feature, that is, it is a power law with an exponential cutoff. Second, we collect blogs in ScienceNet.cn and carry on empirical studies on the interarrival time distribution. The empirical results agree well with the analytical result, obeying a special power law with the exponential cutoff, that is, a special kind of Gamma distribution. These empirical results verify the model, providing an evidence for a new class of phenomena in human dynamics. In human dynamics there are other distributions, besides power-law distributions. These findings demonstrate the variety of human behavior dynamics.

cs.SI↗

arXiv2010

Relationship between exponent of power-law distributions and exponent of cumulative distributions

We commented on Ref.[Andrade J S, Herrmann H J, Andrade R F S, et al. Phys. Rev. Lett. 94, 018702 (2005)] and corrected the approach to estimate the degree distribution of the Apollonian network. However, after reading our manuscript, Herrmann indicated that it was due to a small typographic error and Herrmann et al. published Ref. [Andrade J S, Herrmann H J, Andrade R F S, et al. Phys. Rev. Lett. 102, 079901 (2009)]. In this paper, the relationship between an exponent of power-law distributions and the exponent of cumulative distributions is studied. For power-law distribution with geometrically growing domain, we prove that its exponent is equal to the exponent of its cumulative distribution. We carried out numerical simulations and obtain results that are in good agreement with the theoretical analysis.

physics.gen-ph↗