On the structural properties of small-world networks with range-limited shortcut links

We explore a new variant of Small-World Networks (SWNs), in which an additional parameter (r$r$) sets the length scale over which shortcuts are uniformly distributed. When r=0$r=0$ we have an ordered network, whereas r=1$r=1$ corresponds to the original Watts–Strogatz SWN model. These limited range SWNs have a similar degree distribution and scaling properties as the original SWN model. We observe the small-world phenomenon for r?1$r?1$, indicating that global shortcuts are not necessary for the small-world effect. For limited range SWNs, the average path length changes nonmonotonically with system size, whereas for the original SWN model it increases monotonically. We propose an expression for the average path length for limited range SWNs based on numerical simulations and analytical approximations.     

A self-organizing shortest path finding strategy on complex networks

The shortcomings of traditional methods to find the shortest path are revealed, and a strategy of finding the self-organizing shortest path based on thermal flux diffusion on complex networks is presented. In our method, the shortest paths between the source node and the other nodes are found to be self-organized by comparing node temperatures. The computation complexity of the method scales linearly with the number of edges on underlying networks. The effects of the method on several networks, including a regular network proposed by Ravasz and Barabási which is called the RB network, a real network, a random network proposed by Ravasz and Barabási which is called the ER network and a scale-free network, are also demonstrated. Analytic and simulation results show that the method has a higher accuracy and lower computational complexity than the conventional methods.     

Probability routing strategy for scale-free networks

This study proposes a probability routing strategy for improving traffic capability on scale-free networks. Compared with the shortest path routing strategy depending on central nodes largely and the efficient routing strategy avoiding hub routers as much as possible, the probability routing strategy makes use of hub routers more efficiently, transferring approximate average amount of packs of the whole network. Simulation results indicate that the probability routing strategy has the highest network capacity among the three routing strategies. This strategy provides network capacity that can be more than 30 times higher than that of the shortest path routing strategy and over 50% higher than that of the efficient routing strategy. In addition, the average routing path length of our proposed strategy is over 10% shorter than that of the efficient routing strategy and only about 10% longer than that of the shortest path routing strategy.     

复杂网络病毒传播的局域控制研究

从复杂网络的节点路径长度范围的角度来研究病毒传播的局域控制，分析了在不同拓扑结构的复杂网络中进行局域控制的有效性.研究表明，局域控制对WS小世界网络、BA无标度网络和ER随机网络三类复杂网络均有效，但只有WS小世界网络存在零感染的控制范围最优值d=3；对于长程连边的分布存在距离偏好的Kleinberg小世界网络，随着依赖度的增大，病毒传播率临界值增加，同时局域范围控制的效果得到加强. 关键词： 复杂网络 病毒传播 局域控制 路径长度     

A weight’s agglomerative method for detecting communities in weighted networks based on weight’s similarity

This paper proposes the new definition of the community structure of the weighted networks that groups of nodes in which the edge's weights distribute uniformly but at random between them. It can describe the steady connections between nodes or some similarity between nodes' functions effectively. In order to detect the community structure efficiently, a threshold coefficient κ to evaluate the equivalence of edges' weights and a new weighted modularity based on the weight's similarity are proposed. Then, constructing the weighted matrix and using the agglomerative mechanism, it presents a weight's agglomerative method based on optimizing the modularity to detect communities. For a network with n nodes, the algorithm can detect the community structure in time O(n²log₂ⁿ). Simulations on networks show that the algorithm has higher accuracy and precision than the existing techniques. Furthermore, with the change of κ the algorithm discovers a special hierarchical organization which can describe the various steady connections between nodes in groups.     

A novel method for identifying influential nodes in complex networks based on gravity model

How to identify influential nodes in complex networks is an essential issue in the study of network characteristics. A number of methods have been proposed to address this problem, but most of them focus on only one aspect. Based on the gravity model, a novel method is proposed for identifying influential nodes in terms of the local topology and the global location. This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes, replaces the shortest distance with a probabilistically motivated effective distance, and fully considers the influence of nodes and their neighbors from the aspect of gravity. On eight real-world networks from different fields, the monotonicity index, susceptible-infected-recovered (SIR) model, and Kendall's tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods. The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.     

A novel medium access control protocol for optical local networks based on data request ordering

The basic aim of the scheduling algorithms in wavelength division multiplexing (WDM) single-hop optical networks is to construct a short schedule, in order to support quick communication between the nodes of the network. A design of medium access control (MAC) protocol without collisions is introduced for scheduling variable-length data packets based on a broadcast and select architecture. The system includes only data channels and the coordination of the transmissions is achieved via control packets, functioning before the beginning of actual transmissions. The proposed scheme adopts a prediction mechanism in order to eliminate the possible delay introduced by the scheduling computation between the control and data phases of each cycle of transmission. The two common scheduling strategies suggested try to reorder the service sequence of the nodes, by prioritizing the nodes with long data packets, compared with the nodes with short data packets. The final schedule, formed by a scheduling matrix, seems to be much shorter and leads to an increase, in terms of channel utilization. The extensive simulation results show that the novel scheduling techniques offer more free time space for schedule and allow more data to travel on the medium for the same amount of time. Also, the mean packet delay on the queues is reduced and the relation throughput-delay is much better.     

A self-consistent approach to measure preferential attachment in networks and its application to an inherent structure network

Preferential attachment is one possible way to obtain a scale-free network. We develop a self-consistent method to determine whether preferential attachment occurs during the growth of a network, and to extract the preferential attachment rule using time-dependent data. Model networks are grown with known preferential attachment rules to test the method, which is seen to be robust. The method is then applied to a scale-free inherent structure (IS) network, which represents the connections between minima via transition states on a potential energy landscape. Even though this network is static, we can examine the growth of the network as a function of a threshold energy (rather than time), where only those transition states with energies lower than the threshold energy contribute to the network. For these networks we are able to detect the presence of preferential attachment, and this helps to explain the ubiquity of funnels on potential energy landscapes. However, the scale-free degree distribution shows some differences from that of a model network grown using the obtained preferential attachment rules, implying that other factors are also important in the growth process.     

LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks

Identifying influential nodes in complex networks is one of the most significant and challenging issues, which may contribute to optimizing the network structure, controlling the process of epidemic spreading and accelerating information diffusion. The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity. Moreover, they do not take into account the impact of network topology evolution over time, resulting in limitations in some applications. Based on local information of networks, a local clustering H-index (LCH) centrality measure is proposed, which considers neighborhood topology, the quantity and quality of neighbor nodes simultaneously. The proposed measure only needs the information of first-order and second-order neighbor nodes of networks, thus it has nearly linear time complexity and can be applicable to large-scale networks. In order to test the proposed measure, we adopt the susceptible-infected-recovered (SIR) and susceptible-infected (SI) models to simulate the spreading process. A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.     

A temporal ant colony optimization approach to the shortest path problem in dynamic scale-free networks

A large number of networks in the real world have a scale-free structure, and the parameters of the networks change stochastically with time. Searching for the shortest paths in a scale-free dynamic and stochastic network is not only necessary for the estimation of the statistical characteristics such as the average shortest path length of the network, but also challenges the traditional concepts related to the “shortest path” of a network and the design of path searching strategies. In this paper, the concept of shortest path is defined on the basis of a scale-free dynamic and stochastic network model, and a temporal ant colony optimization (TACO) algorithm is proposed for searching for the shortest paths in the network. The convergence and the setup for some important parameters of the TACO algorithm are discussed through theoretical analysis and computer simulations, validating the effectiveness of the proposed algorithm.     

A novel approach for multiple mobile objects path planning: Parametrization method and conflict resolution strategy

We present a new approach containing two steps to determine conflict-free paths for mobile objects in two and three dimensions with moving obstacles. Firstly, the shortest path of each object is set as goal function which is subject to collision-avoidance criterion, path smoothness, and velocity and acceleration constraints. This problem is formulated as calculus of variation problem (CVP). Using parametrization method, CVP is converted to time-varying nonlinear programming problems (TNLPP) and then resolved. Secondly, move sequence of object is assigned by priority scheme; conflicts are resolved by multilevel conflict resolution strategy. Approach efficiency is confirmed by numerical examples.     

A novel block encryption scheme based on chaos and an S-box for wireless sensor networks

The wireless sensor network (WSN) has been widely used in various fields, but it still remains in the preliminary discovery and research phase with a lack of various related mature technologies. Traditional encryption schemes are not suitable for wireless sensor networks due to intrinsic features of the nodes such as low energy, limited computation capability, and lack of storage resources. In this paper, we present a novel block encryption scheme based on the integer discretization of a chaotic map, the Feistel network structure, and an S-box. The novel scheme is fast, secure, has low resource consumption and is suitable for wireless sensor network node encryption schemes. The experimental tests are carried out with detailed analysis, showing that the novel block algorithm has a large key space, very good diffusion and disruptive performances, a strict avalanche effect, excellent statistical balance, and fast encryption speed. These features enable the encryption scheme to pass the SP800-22 test. Meanwhile, the analysis and the testing of speed, time, and storage space on the simulator platform show that this new encryption scheme is well able to hide data information in wireless sensor networks.     

A novel method for sacrificial layer release in MEMS devices fabrication

During the forming process of the free-standing structure or the functional cavity when releasing the high aspect ratio sacrificial layer,such structures tend to stick to the substrate due to capillary force.This paper describes the application of pull-in length conception as design rules to a novel ’dimpled’ method in releasing sacrificial layer.Based on the conception of pull-in length in adhering phenomenon,the fabrication and releasing sacrificial layer methods using micro bumps based on the silicon substrate were presented.According to the thermal isolation performances of one kind of micro electromechanical system device thermal shear stress sensor,the sacrificial layers were validated to be successfully released.     

Nanophotonic waveguidance in quantum networks - A simulation approach for quantum state transfer

A cavity-assisted Raman process can initialize the inter-conversion of stationary spin qubits and flying photon qubits in quantum channels. The qubit transmission essentially requires the implementation of special laser fields to excite atoms at the transmitting node of the quantum cavity. The flying qubit is ultimately absorbed at the receiving node of the channel to regenerate the original spin state of the nanodot. The present paper deals with the phenomena involved in such nanophotonic waveguidance by the process of rigorous simulation, and it is reported that the results obtained by implementing suitable transmission protocol reflect well the reliable transfer/entanglement of the quantum states of the nanodot qubit.     

A fault-tolerant small world topology control model in ad hoc networks for search and rescue

Due to their self-organized, multi-hop and distributed characteristics, ad hoc networks are useful in search and rescue. Topology control models need to be designed for energy-efficient, robust and fast communication in ad hoc networks. This paper proposes a topology control model which specializes for search and rescue—Compensation Small World-Repeated Game (CSWRG)—which integrates mobility models, constructing small world networks and a game-theoretic approach to the allocation of resources. Simulation results show that our mobility models can enhance the communication performance of the constructed small-world networks. Our strategy, based on repeated game, can suppress selfish behavior and compensate agents that encounter selfish or faulty neighbors. This model could be useful for the design of ad hoc communication networks.     

A thermal flux-diffusing model for complex networks and its applications in community structure detection

We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally, several real-world networks are investigated.     

A decentralized flow redistribution algorithm for avoiding cascaded failures in complex networks

A decentralized random algorithm for flow distribution in complex networks is proposed. The aim is to maintain the maximum flow while satisfying the flow limits of the nodes and links in the network. The algorithm is also used for flow redistribution after a failure in (or attack on) a complex network to avoid a cascaded failure while maintaining the maximum flow in the network. The proposed algorithm is based only on the information about the closest neighbours of each node. A mathematically rigorous proof of convergence with probability 1 of the proposed algorithm is provided.     

A uniform framework of projection and community detection for one-mode network in bipartite networks

Projection is a widely used method in bipartite networks. However, each projection has a specific application scenario and differs in the forms of mapping for bipartite networks. In this paper, inspired by the network-based information exchange dynamics, we propose a uniform framework of projection. Subsequently, an information exchange rate projection based on the nature of community structures of a network(named IERCP) is designed to detect community structures of bipartite networks. Results from the synthetic and real-world networks show that the IERCP algorithm has higher performance compared with the other projection methods. It suggests that the IERCP may extract more information hidden in bipartite networks and minimize information loss.     

A fast and efficient heuristic algorithm for detecting community structures in complex networks

Community structure is an important property of complex networks. How to detect the communities is significant for understanding the network structure and to analyze the network properties. Many algorithms, such as K-L and GN, have been proposed to detect community structures in complex networks. According to daily experience, a community should have many nodes and connections. Based on these principles and existing researches, a fast and efficient algorithm for detecting community structures in complex networks is proposed in this paper. The key strategy of the algorithm is to mine a node with the closest relations with the community and assign it to this community. Four real-world networks are used to test the performance of the algorithm. Experimental results demonstrate that the algorithm proposed is rather efficient for detecting community structures in complex networks.