Earticle

현대 인프라 네트워크는 원래 독립적이었던 전력, 교통, 통신 및 에너지 네트워크 등이 점차 서로 얽히면서 복잡한 시스템 이 형성된다. 그러나 이 네트워크들의 통합 과정은 대부분 경험과 지리적 환경에 의존하며, 체계적이고 정량적인 이론적 지원 이 부족하다. 본 연구는 전력-천연가스 핵심 도시 인프라를 대상으로 "상호 의존 특성 지표" 알고리즘을 활용하여 각각 랜덤, 동질 및 이질 상호 의존 네트워크 모델을 구축하였다. 이 세 가지 상호 의존 패턴의 성능과 견고성을 비교하기 위해 네 가지 네트워크 공격 전략과 세 가지 견고성 평가 지표를 포함하는 평가 체계를 설계하였다. 실험 결과, "상호 의존 특성 지표" 알고 리즘을 기반으로 구축된 동질 상호 의존 패턴은 랜덤 및 이질 상호 의존 패턴과 비교했을 때, 각종 네트워크 공격에 대응하는 데 있어 뛰어난 견고성과 성능을 보여주었다. 종합적인 평가 척도를 통해 최적의 상호 의존 패턴에 가장 근접한 것으로 확인 되었다.

Modern infrastructure networks are complex systems formed as power, transportation, communication, energy networks, which were originally independent, gradually intertwine with each other. However, the integration process of these networks mostly relies on experience and geographical environment, and there is a lack of systematic and quantitative theoretical support. This study focuses on the power-natural gas, which is the core urban infrastructure. By applying the "Interdependent Characteristic Index" algorithm, it constructs the random, homogeneous, and heterogeneous interdependent network models respectively. To compare the performance and robustness of these three interdependent patterns, an evaluation system that includes four network attack strategies and three robustness evaluation indicators is designed. The experimental results show that, when compared with the random and heterogeneous interdependent patterns, the homogeneous interdependent pattern constructed based on the "Interdependent Characteristic Index" algorithm demonstrates excellent robustness and performance in dealing with various network attacks. Through a comprehensive evaluation metric, it is confirmed that this pattern is the closest to the optimal interdependent pattern.

인류는 다양한 복잡계(Complex Networks) 속에서 살아가고 있어서 이를 잘 해석하는 것은 자연계에서의 대두되는 문제를 해결하는 실마리를 찿을 수 있는 길이라고 본다. 트위트, 페이스북 등과 같은 소셜네트워크망의 구성도, 항공기 등 교통수단 구성도, 각종 통신선로망, 유전자의 대사작용, 질병의 발병/전파도, 비즈니스 모델 구성 등이 복잡계(Complex Networks)의 사례들이다. 인문사회 또는 자연과학 분야, 공학 분야 등 전분야에서 복잡계를 쉽게 분석 또는 설명하고자 많은 이론과 모델(ER Random network 모델, SIR 모델, Watts-Strogatz 모델, Chung-Lu 모델, Preferential Attachment 모델 등)이 개발되어 사용되어 지고 있으나 복잡계를 완전하게 표시하지는 못하고 있다. 이를 직관적으로 이해할 수 있도록 PCB(Propagation Communication Board; 정보 전파 보드) 모델을 제안하였다. 이 제안 모델을 검증하기 위해서 복잡계에 적용 가능한 사례를 도입하여 검증을 해 보았다. 우선적으로 교통/운송망에 적용하여 정보 전달이 어떻게 이루어지는지 살펴보고, 유무선 통신망에서의 정보 전달 과정을, 마지막으로 홍보 매체에서의 홍보용 정보가 어떻게 전달되는지를 보았다. 본 논문에서는 언급하지 않은 질병 또는 전염병의 전달 경로 등 다른 복잡계에서도 본 제안 모델이 적용 가능하다.

Optimizing complex networks to be resilient against various attack models has been an important problem that is actively studied in the academia. In the proposed optimization method, individual node degrees are balanced iteratively based on the No-Regret learning algorithm, resulting in a robust network topology with increased resilience against outside attacks. Through simulation results, we show that the proposed robustness enhanced networks perform well under targeted attacks compared to the conventional optimized networks.

It has been thirty years since Daedeuk Science Town was established in Daejeon, Korea. As a main purpose of establishing the Science Town was to promote basic research, there have not been much economic impacts with little networks among research and educational institutes within the science park and the city of Daejeon in general. After the economic crisis in 1997, however, there have been increasing changes around the Town. Since then more than 400 technology-intensive firms were newly created, mainly from the research institutes, or introduced to the Park area. This paper analyzes R&D networks of these small firms, based on a questionnaire survey. The paper has argued: 1) to establish technology transfer centers, 2) to emphasize research related to commercialization, 3) to intensify advertizement of research outputs to the potential entrepreneurs, 4) to assist entrepreneural associations of concerned companies.

As a result of the spreading various types of nodes and topological complexity of the cloud computing, it is an backbreaking task to characterize the nodes which are far more influential than others. The objective of our paper is to examine complex networking based cloud computing. Complex networks are a type of networks whose structure is irregular, complex and evolving dynamically. Its main goal is the analysis of small networks to that particular of system with millions of nodes. It has been found that none of the technique is way better for cloud computing. Therefore, this paper ends with the future scope to solve these issues.

As a result of stochasticity of the spreading process and topological complexity of the cloud computing, it is an arduous task to characterize the nodes which are far more influential than others. The entire objective of this paper is to examine complex networking based cloud computing. Complex networks are those networks whose structure is irregular, complex and evolving dynamically. Its main focus may be the analysis of small networks to that particular of system with thousands or millions of node. It has been found from the review that none of the technique is way better for cloud computing. Therefore, this paper ends with the future scope to solve these issues.

According to the defects that community detection algorithm in unknown complex networks has a pre-parameter. We propose Adaptive Label Propagation Algorithm (ALPA) to detect community structures in complex networks. The ALPA algorithm find out all disjoint Maximal Clique (MC) and let each MC share the identical weight and unique label so as to reduce the redundant labels and uncontrollable factors. The stability of ALPA algorithm is enhanced by synchronous update during iterations. Meanwhile it will converge easily due to the termination condition that all of the vertexes have the label. During iterations we use the adaptive threshold method to overcome the pre-parameter limitation. Compared with other community detection algorithms in synthetic networks and real networks, our experiments show that ALPA algorithm not only improves the tolerance of mixing parameter, but also enhances its robustness.

For a better predicting and analyses of robustness and security properties of some networks examples such as scale-free networks, small-world networks and real-world router-level Internet, models with different topologies and variable loads of the corresponding networks were founded firstly. Then, experiments of hybrid attacks ranging from complete random attacks where attack parameter tao=1 to complete targeted attacks where tao=0 were simulated. Results showed clearly that scale-free networks were robust to random failures and quite fragile to target attacks and the progression of networks fragility seems to have power-law distribution with workload parameter. For the NW small-world networks, attacks types are not so much sensitive to the robustness of the networks. What’s found here would be useful for design and implement of some real-world networks.

It has shown that the betweenness distribution of shortest path (SP) routing strategy on scale-free networks is exponential. Some nodes with great degree have higher flow. It is very easy to cause cascading failure when the node's flow exceeds its processing capacity. As a result, the service quality will fall. Utilizing the BA scale-free networks, we study the cascading failure between the minimum information path (MIP) routing strategy and the shortest path (SP) routing strategy. Then the tolerance parameters with upper and lower threshold are obtained by theory. The conclusion displays that the performance of SP routing strategy is slightly better than the MIP routing strategy when the network has little processing capacity. However, the value G of MIP rapidly growths and is close to 1, but the value of SP growths slowly with the process capacity increasing. In a word, the performance of MIP routing strategy is obviously better than the SP routing strategy. Therefore the robustness can be improved greatly by increasing the process capacity of some nodes under MIP routing policy.

This paper proposes the community detection in complex networks based on improved genetic algorithm and local optimization (IGALO) in terms of the defect that traditional community detection approaches based on genetic algorithm have strong randomness and weak searching ability in the process of community detection. Taking modularity function Q as the objective function, IGALO algorithm adopts label propagation method of one-iteration to initialize population so as to generate initial population with certain precision. Then, anti-destructive one-way crossover strategy is proposed to ensure the crossover operation to develop in the direction of making community structure increase to modularity function. Finally, mutation strategy of node local optimization is proposed to improve the searching efficiency of algorithm. This algorithm effectively overcomes the defect that traditional algorithms have weak searching ability and improves the community detection accuracy. Tests are made on benchmark networks and real-world networks and comparative analysis is also made with various classic algorithms. The results show that IGALO algorithm is effective and feasible.

This paper addresses the outer synchronization problem of complex networks with multiple coupling time-varying delays. First of all, some novel synchronization criteria are obtained by employing the Lyapunov stability theory and linear matrix inequality (LMI), we design some synchronization controllers to keep the given complex network synchronizing to the object trajectory. Furthermore, an adaptive outer synchronization scheme is derived to achieve global synchronization, which is simpler than some traditional controllers. Moreover, the presented results here can also be applied to complex networks with single time delay case. Finally, numerical analysis and simulations for two coupled complex networks which are composed of unified chaotic systems are given to demonstrate the effectiveness and feasibility of the proposed complex network control and synchronization schemes.

A Web service is a well defined concept of a set of computational or physical activities involving a number of resources, designed to fulfill the needs of customers or for business requirement. Community structure is the most common topological structure in complex networks. A significant characteristic of complex networks is that they are generally composed of highly inner connected sub networks called community. It is very helpful and important to understand the features of these communities. This paper has focused on the community structure of structural service networks formed by public web services available on the Internet. The overall objective is to enhance the cloud web services using complex networks.

The rapid growth of the large-scale World-Wide Web poses great challenge to existing focused crawling methods. Whetheranalyzing text content or link structure, traditional focused crawler were mainly based on the page granularity. Random walking in the network composed of a large number of pages, the focused crawler is easy to get lost. Obviously, narrowing the focused crawling range from the entireWeb can improve the precision and efficiency. A focused crawling method based on the twogranularitiesis put forward. Firstly, using detectingcommunity algorithm to analyze the link structure of the network composed of websites, a given topic web sites group is built up. It contributes to narrow the crawling range. Secondly, all topic relevant analysis for web pages and link prediction are performed inside this generated group. Topic relevant analysis is implemented through calculating the topic similarity for title and content separately. The similarity of father pages, anchor texts and the string text for URL all are considered to predict the topic relevance for unknown links.The experimental results suggest that this method is very effective for given topic, and it can improve the precision.

A fuzzy integral sliding mode control method based on the finite time stable theory and integral sliding mode control theory is proposed to solve a class of complex network problems. The complex networks are expressed by T-S fuzzy models with bounded approximation errors by using the approximation capability of T-S fuzzy models. The paper proposes fuzzy dynamic integral sliding mode control scheme and the scheme is designed for the complex networks based on their T-S fuzzy approximation models. The simulation result shows that the trajectories of the closed-loop control system are kept on the sliding surface almost since the initial time. And the sliding motion of the closed-loop control system involving the original nonlinear system can be shown to be semi globally asymptotically stable. The controller can make system convergence in finite time and reduce the system buffeting effectively.

The majority of recent embedded systems are based on MPSoCs (Multi-Processors System on Chip) architectures. The topologies and the interconnections inside multi processors almost adopt NoCs (Networks on Chip) whose topology and task scheduling algorithm have a direct impact on its performances. In this paper, by using static data flow, a task scheduling algorithm which would automatically assign the application tasks onto different processors is proposed based on complex network. The goal of our algorithm is to replace the static data flow subnetwork by a single dynamic data flow actor such that the global performance in terms of latency and throughput is optimized. Through complex network, it greatly enhances the power of our algorithm in terms of avoiding deadlock, saving energy and providing for integration with more general models of computation. Experimental results show up to 60% performance improvement for real-world examples.

Although localization has been widely studied for Wireless Sensor Networks (WSNs), the complex environments and the large network scale pose severe challenges and make it necessary to develop new reliable localization algorithms. In this paper, we propose a novel Multi-Hop Localization Algorithm for large-scale WSNs in complex environments. This work is based on the consideration that the localization process would encounter several kinds of adverse factors with different nature at the same time (e.g. anisotropic network characteristics, ranging uncertainty, link quality of multihop paths, etc.), which lead to obvious degradation of localization performance. Unlike most of the existing schemes, we transform the localization problem in complex environments into a hybrid constraint satisfaction problem (CSP) which is composed of three different kinds of constraints, i.e. spatial constraint, network situation constraint, and confidence constraint. Set-membership approach and interval analysis method have been utilized to deal with the CSP and determine the positions of sensor nodes. Simulation results show that our scheme is an effective and efficient approach to localization in large-scale WSNs.

In the paper, a class of complex dynamical network with non-delayed and delayed coupling is investigated. Then the problem of synchronization stability analysis for the complex dynamical network is further discussed. Taking advantage of linear matrix inequalities (LMI), a novel Lyapunov function is constructed. And then a new general stability criterion is proposed of synchronization state in the complex dynamical networks. Finally, numerical simulations are provided to show the effectiveness and feasibility of the proposed theorem.

In recent years, researchers found that the complex dynamical network is an effective tool to describe and understand the complex nonlinear systems. Because of the difficulty in establishing accurate mathematical models for nonlinear systems, the classical linear control methods are difficult to be applied to them. T-S fuzzy model has been proved to be an all-purpose approximation for nonlinear system, and has ability to combine linear control theory with fuzzy theory. In this paper, a scheme for synchronization between two fuzzy networks based on output signals under pinning control is proposed. Unlike other common methods,it only requires output signals to be transmitted from driving network to response network, and part of nodes in response network to be controlled. The synchronization criteria is given by using Lyapunov stability theory and linear matrix inequality technology, and the number of pinning nodes can be calculated by using the proposed synchronization theorem. Finally, a simulation example is given to verify the effectiveness of this scheme.

The complex social network theory is applied to solve the group doctor-patient conflict in present paper. Results showed that there were different interests in the evolution process of doctor-patient conflict. A complex network structure was developed around core interests and core nodes in the process of doctor-patient conflict. And this network structure can be reinforced under the law of "robustness" and "self organized criticality" .With the probability of the occurrence of events, the model of social behavior will change suddenly and make the events out of control.