Scale-free networks without growth

In this paper, we proposed an ungrowing scale-free network model, indicating the growth may not be a necessary condition of the self-organization of a network in a scale-free structure. The analysis shows that the degree distributions of the present model can varying from the Poisson form to the power-law form with the decrease of a free parameter α. This model provides a possible mechanism for the evolution of some scale-free networks with fixed size, such as the friendship networks of school children and the functional networks of the human brain.     

How people make friends in social networking sites—A microscopic perspective

We study the detailed growth of a social networking site with full temporal information by examining the creation process of each friendship relation that can collectively lead to the macroscopic properties of the network. We first study the reciprocal behavior of users, and find that link requests are quickly responded to and that the distribution of reciprocation intervals decays in an exponential form. The degrees of inviters/accepters are slightly negatively correlative with reciprocation time. In addition, the temporal feature of the online community shows that the distributions of intervals of user behaviors, such as sending or accepting link requests, follow a power law with a universal exponent, and peaks emerge for intervals of an integral day. We finally study the preferential selection and linking phenomena of the social networking site and find that, for the former, a linear preference holds for preferential sending and reception, and for the latter, a linear preference also holds for preferential acceptance, creation, and attachment. Based on the linearly preferential linking, we put forward an analyzable network model which can reproduce the degree distribution of the network. The research framework presented in the paper could provide a potential insight into how the micro-motives of users lead to the global structure of online social networks.     

Effects of average degree of network on an order-disorder transition in opinion dynamics

We have investigated the influence of the average degree \langle k \rangle of network on the location of an order--disorder transition in opinion dynamics. For this purpose, a variant of majority rule (VMR) model is applied to Watts--Strogatz (WS) small-world networks and Barab\'{a}si--Albert (BA) scale-free networks which may describe some non-trivial properties of social systems. Using Monte Carlo simulations, we find that the order--disorder transition point of the VMR model is greatly affected by the average degree \langle k \rangle of the networks; a larger value of \langle k \rangle results in a more ordered state of the system. Comparing WS networks with BA networks, we find WS networks have better orderliness than BA networks when the average degree \langle k \rangle is small. With the increase of \langle k \rangle, BA networks have a more ordered state. By implementing finite-size scaling analysis, we also obtain critical exponents \beta/\nu, \gamma/\nu and 1/\nu for several values of average degree \langle k \rangle. Our results may be helpful to understand structural effects on order--disorder phase transition in the context of the majority rule model.     

Behavior patterns of online users and the effect on information filtering

Understanding the structure and evolution of web-based user-item bipartite networks is an important task since they play a fundamental role in online information filtering. In this paper, we focus on investigating the patterns of online users’ behavior and the effect on recommendation process. Empirical analysis on the e-commercial systems show that users’ taste preferences are heterogeneous in general but their interests for niche items are highly clustered. Additionally, recommendation processes are investigated on both the real networks and the reshuffled networks in which real users’ behavior patterns can be gradually destroyed. We find that the performance of personalized recommendation methods is strongly related to the real network structure. Detailed study on each item shows that most hot items are accurately recommended and their recommendation accuracy is robust to the reshuffling process. However, the accuracy for niche items is relatively low and drops significantly after removing users’ behavior patterns. Our work is also meaningful in practical sense since it reveals an effective direction to improve the accuracy and the robustness of the existing recommender systems.     

Getting Ahead of the Arms Race: Hothousing the Coevolution of VirusTotal with a Packer

Malware detection is in a coevolutionary arms race where the attackers and defenders are constantly seeking advantage. This arms race is asymmetric: detection is harder and more expensive than evasion. White hats must be conservative to avoid false positives when searching for malicious behaviour. We seek to redress this imbalance. Most of the time, black hats need only make incremental changes to evade them. On occasion, white hats make a disruptive move and find a new technique that forces black hats to work harder. Examples include system calls, signatures and machine learning. We present a method, called Hothouse, that combines simulation and search to accelerate the white hat’s ability to counter the black hat’s incremental moves, thereby forcing black hats to perform disruptive moves more often. To realise Hothouse, we evolve EEE, an entropy-based polymorphic packer for Windows executables. Playing the role of a black hat, EEE uses evolutionary computation to disrupt the creation of malware signatures. We enter EEE into the detection arms race with VirusTotal, the most prominent cloud service for running anti-virus tools on software. During our 6 month study, we continually improved EEE in response to VirusTotal, eventually learning a packer that produces packed malware whose evasiveness goes from an initial 51.8% median to 19.6%. We report both how well VirusTotal learns to detect EEE-packed binaries and how well VirusTotal forgets in order to reduce false positives. VirusTotal’s tools learn and forget fast, actually in about 3 days. We also show where VirusTotal focuses its detection efforts, by analysing EEE’s variants.     

Binary choices in small and large groups: A unified model

Two different ways to model the diffusion of alternative choices within a population of individuals in the presence of social externalities are known in the literature. While Galam’s model of rumors spreading considers a majority rule for interactions in several groups, Schelling considers individuals interacting in one large group, with payoff functions that describe how collective choices influence individual preferences. We incorporate these two approaches into a unified general discrete-time dynamic model for studying individual interactions in variously sized groups. We first illustrate how the two original models can be obtained as particular cases of the more general model we propose, then we show how several other situations can be analyzed. The model we propose goes beyond a theoretical exercise as it allows modeling situations which are relevant in economic and social systems. We consider also other aspects such as the propensity to switch choices and the behavioral momentum, and show how they may affect the dynamics of the whole population.     

Evolving hypernetwork model

Complex hypernetworks are ubiquitous in real-life systems. While a substantial body of previous research has only focused on the applications of hypernetworks, relatively little work has investigated the evolving models of hypernetworks. Considering the formations of many real world networks, we propose two evolving mechanisms of the hyperedge growth and the hyperedge preferential attachment, then construct an evolving hypernetwork model. We introduce some basic topological quantities, such as a variety of degree distributions, clustering coefficients as well as average path length. We numerically investigate these quantities in the limit of large hypernetwork size and find that our hypernetwork model shares similar qualitative features with the majority of complex networks that have been previously studied, such as the scale-free property of the degree distribution and a high degree of clustering, as well as the small-world property. It is expected that our attempt in the hypernetwork model can bring the upsurge in the study of the hypernetwork model in further.     

Graph kernels,hierarchical clustering,and network community structure: experiments and comparative analysis

There has been a quickly growing interest in properties of complex networks, such as the small world property, power-law degree distribution, network transitivity, and community structure, which seem to be common to many real world networks. In this study, we consider the community property which is also found in many real networks. Based on the diffusion kernels of networks, a hierarchical clustering approach is proposed to uncover the community structure of different extent of complex networks. We test the method on some networks with known community structures and find that it can detect significant community structure in these networks. Comparison with related methods shows the effectiveness of the method.     

The reconstruction of Rh(001) upon oxygen adsorption

We report on a first-principles study of the structure of O/Rh(001) at half a monolayer of oxygen coverage, performed using density functional theory. We find that oxygen atoms sit at the center of the black squares in a chess-board c(2×2) pattern. This structure is unstable against a rhomboid distortion of the black squares, which shortens the distance between an O atom and two of the four neighboring Rh atoms, while lengthening the distance with respect to the other two. We actually find that the surface energy is further lowered by allowing the O atom to get off the short diagonal of the rhombus thus formed. We predict that the latter distortion is associated with an order–disorder transition, occurring below room temperature. The above rhomboid distortion of the square lattice may be seen as a rotation of the empty white squares. Our findings are at variance with recent claims based on STM images, according to which it is instead the black squares which would rotate. We argue that these images are indeed compatible with our predicted reconstruction pattern.     

Charged BTZ-like black holes in higher dimensions

Motivated by many worthwhile papers about (2+1)-dimensional BTZ black hole solutions, we generalize them to (n+1)-dimensional solutions, the so-called BTZ-like solutions. We show that the electric field of BTZ-like solutions is the same as that of (2+1)-dimensional BTZ black holes, and also their lapse functions are approximately the same, too. By these similarities, it is also interesting to investigate the geometric and thermodynamics properties of the BTZ-like solutions. We find that, depending on the metric parameters, the BTZ-like solutions may be interpreted as black hole solutions with inner (Cauchy) and outer (event) horizons, an extreme black hole or naked singularity. Then, we obtain the conserved and thermodynamic quantities, and we show that they satisfy the first law of thermodynamics. Next, we perform a thermodynamic stability analysis in the canonical ensemble and find that the BTZ-like solutions are stable in the whole phase space.     

Bayes-optimal solution to inverse halftoning based on statistical mechanics of the Q-Ising model

On the basis of statistical mechanics of the Q-Ising model, we formulate the Bayesian inference to the problem of inverse halftoning, which is the inverse process of representing gray-scales in images by means of black and white dots. Using Monte Carlo simulations, we investigate statistical properties of the inverse process, especially, we reveal the condition of the Bayes-optimal solution for which the mean-square error takes its minimum. The numerical result is qualitatively confirmed by analysis of the infinite-range model. As demonstrations of our approach, we apply the method to retrieve a grayscale image, such as standard image Lena, from the halftoned version. We find that the Bayes-optimal solution gives a fine restored grayscale image which is very close to the original. In addition, based on statistical mechanics of the Q-Ising model, we are sucessful in constructing a practically useful method of inverse halftoning using the Bethe approximation.      

Thermodynamic surface system of static black holes and area law

We propose a new picture of black holes through a special holographic screen. This holographic screen contains all the degrees of freedom of a black hole. We find that this holographic screen is similar to the ordinary thermodynamic surface system. Meanwhile, through the "white-wall box" and the formula of sound velocity, we find some similarities between gravitons and photons. We further assume that such a holographic screen is a kind of Bose-Einstein condensate of gravitons. Through this assumption and those similarities, we finally get the area law of static black holes.     

Thermodynamics of higher dimensional black holes with higher order thermal fluctuations

In this paper, we consider higher order corrections of the entropy, which coming from thermal fluctuations, and find their effect on the thermodynamics of higher dimensional charged black holes. Leading order thermal fluctuation is logarithmic term in the entropy while higher order correction is proportional to the inverse of original entropy. We calculate some thermodynamics quantities and obtain the effect of logarithmic and higher order corrections of entropy on them. Validity of the first law of thermodynamics investigated and Van der Waals equation of state of dual picture studied. We find that five-dimensional black hole behaves as Van der Waals, but higher dimensional case have not such behavior. We find that thermal fluctuations are important in stability of black hole hence affect unstable/stable black hole phase transition.     

Minimal mechanisms for school formation in self-propelled particles

In the context of social organisms, a school refers to a cohesive group of organisms that share a common speed and direction of motion, as well as a common axis of body alignment or polarization. Schools are also noted for the relatively fixed nearest-neighbour distances between individuals. The rules of interaction that lead to the formation and maintenance of a school structure have been explored experimentally, analytically, and by simulation. Interest in biological examples, and non-biological “self-propelled particles” such as robots, vehicles, or autonomous agents leads to the question of what are the simplest possible sets of rules that can assure the formation and the stability of the “perfect school”: an aggregate in which the nearest-neighbour distances and speeds are identical.Here we explore mechanisms that lead to a perfect school structure in one and two dimensions. We consider distance-detection as well as velocity-detection between the interacting pairs of self-propelled particles. We construct interaction forces and formulate schooling equations. In the simplest cases, these equations have analytic solutions. In many cases, the stability of the perfect school can be explored. We then investigate how these structures form and evolve over time from various initial configurations using simulations. We study the relationship between the assumed interaction forces and the school patterns that emerge. While true biological schools are far from perfect, the insights gained from this investigation can help to understand some properties of real schools, and to suggest the appropriate properties of artificial schools where coordinated motion is desired.     

Impact of core-periphery structure on cascading failures in interdependent scale-free networks

Core-periphery structure is a typical meso-scale structure in networks. Previous studies on core-periphery structure mainly focus on the improvement of detection methods, while the research on the impact of core-periphery structure on cascading failures in interdependent networks is still missing. Therefore, we investigate the cascading failures of interdependent scale-free networks with different core-periphery structures and coupling preferences in the paper. First, we introduce an evaluation index to calculate the goodness of core-periphery structure. Second, we propose a new scale-free network evolution model, which can generate tunable core-periphery structures, and its degree distribution is analyzed mathematically. Finally, based on a degree-load-based cascading failure model, we mainly investigate the impact of goodness of core-periphery structure on cascading failures in both symmetrical and asymmetrical interdependent networks. Through numerical simulations, we find that with the same average degree, the networks with weak core-periphery structure will be more robust, while the initial load on node will influence the improvement of robustness. In addition, we also find that the inter-similarity coupling performs better than random coupling. These findings may be helpful for building resilient interdependent networks.     

The spreading of opposite opinions on online social networks with authoritative nodes

The study of opinion dynamics, such as spreading and controlling of rumors, has become an important issue on social networks. Numerous models have been devised to describe this process, including epidemic models and spin models, which mainly focus on how opinions spread and interact with each other, respectively. In this paper, we propose a model that combines the spreading stage and the interaction stage for opinions to illustrate the process of dispelling a rumor. Moreover, we set up authoritative nodes, which disseminate positive opinion to counterbalance the negative opinion prevailing on online social networking sites. With analysis of the relationship among positive opinion proportion, opinion strength and the density of authoritative nodes in networks with different topologies, we demonstrate that the positive opinion proportion grows with the density of authoritative nodes until the positive opinion prevails in the entire network. In particular, the relationship is linear in homogeneous topologies. Besides, it is also noteworthy that initial locations of the negative opinion source and authoritative nodes do not influence positive opinion proportion in homogeneous networks but have a significant impact on heterogeneous networks. The results are verified by numerical simulations and are helpful to understand the mechanism of two different opinions interacting with each other on online social networking sites.     

Coupled disease–behavior dynamics on complex networks: A review

It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease–behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease–behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.     

Phase transitions in four-dimensional AdS black holes with a nonlinear electrodynamics source

In this work we consider black hole solutions to Einstein's theory coupled to a nonlinear power-law electromagnetic field with a fixed exponent value. We study the extended phase space thermodynamics in canonical and grand canonical ensembles, where the varying cosmological constant plays the role of an effective thermodynamic pressure. We examine thermodynamical phase transitions in such black holes and find that both first- and second-order phase transitions can occur in the canonical ensemble while, for the grand canonical ensemble, Hawking–Page and second-order phase transitions are allowed.     

Entropy and supersymmetry of D-dimensional extremal electric black holes versus string states

Following the work of Sen, we consider the correspondence between extremal black holes and string states in the context of the entropy. We obtain and study properties of electrically charged black hole backgrounds of tree level heterotic string theory compactified on a p-dimensional torus, for D = (10 − p) = 4,…,9. We study in particular a one-parameter extremal class of these black holes, the members of which are shown to be supersymmetric. We find that the entropy of such an extremal black hole, when calculated at the stringy stretched horizon, scales in such a way that it can be identified with the entropy of the elementary string state with the corresponding quantum numbers.     

Close Relationships: A Study of Mobile Communication Records

Mobile phone communication as digital service generates ever-increasing datasets of human communication actions, which in turn allow us to investigate the structure and evolution of social interactions and their networks. These datasets can be used to study the structuring of such egocentric networks with respect to the strength of the relationships by assuming direct dependence of the communication intensity on the strength of the social tie. Recently we have discovered that there are significant differences between the first and further “best friends” from the point of view of age and gender preferences. Here we introduce a control parameter p _max based on the statistics of communication with the first and second “best friend” and use it to filter the data. We find that when p _max is decreased the identification of the “best friend” becomes less ambiguous and the earlier observed effects get stronger, thus corroborating them.