Integrating network structure and dynamic information for better routing strategy on scale-free networks

We study information packet routing processes on scale-free networks by mimicking the Internet traffic delivery strategies. We incorporate both the global network structure information and local queuing information in the dynamic processes. We propose several new routing strategies to guide the packet routing. The performance of the routing strategies is measured by the average transit time of the packets as well as their dependence on the traffic amount. We find that the routing strategies which integrate both global network structure information and local dynamic information perform much better than the traditional shortest-path routing protocol which takes into account only the global topological information. Moreover, from comparative studies of these routing strategies, we observe that some of our proposed methods can decrease the average transit time of packets but the performance is closely dependent on the total amount of traffic while some other proposed methods can have good performance independent of the total amount of traffic with hyper-excellent average transit time of packets. Also, numerical results show that our proposed methods integrating network structure information and local dynamic information can work much better than the methods recently proposed in [S. Sreenivasan, R. Cohen, E. López, Z. Toroczkai, H.E. Stanley, Phys. Rev. E 75 (2007) 036105, Zhi-Xi Wu, Gang Peng, Eric W.M. Wong, Kai-Hau Yeung, J. Stat. Mech. (2008) P11002.], which only considered network structure information.     

A packet routing strategy using neural networks on scale-free networks

We propose a dynamic packet routing strategy by using neural networks on scale-free networks. In this strategy, in order to determine the nodes to which the packets should be transmitted, we use path lengths to the destinations of the packets, and adjust the connection weights of the neural networks attached to the nodes from local information and the path lengths. The performances of this strategy on scale-free networks which have the same degree distribution and different degree correlations are compared to one another. Our numerical simulations confirm that this routing strategy is more effective than the shortest path based strategy on scale-free networks with any degree correlations and that the performance of our strategy on assortative scale-free networks is better than that on disassortative and uncorrelated scale-free networks.     

Efficient packet routing strategy in complex networks

We investigate a new efficient packet routing strategy which mitigates traffic congestion on complex networks. In order to avoid congestion, we minimize the maximum betweenness, which is a measure for concentration of routing paths passing through a node in the network. Danila et al. propose a packet routing strategy in which, instead of shortest paths, they used efficient paths, which are the paths with the minimum total summations of weights assigned to nodes in the respective paths. They use a heuristic algorithm in which the weights are updated step by step by using the information of betweenness of each node in every step and the respective total summations of weights for paths through the nodes with large degrees become comparatively large. Thus passage through such nodes, where congestion almost occurs, is likely to be avoided in their algorithm. The convergence time by their algorithm is, however, quite long. In this paper, we propose a new efficient heuristic algorithm which balances traffic on networks by achieving minimization of the maximum betweenness in the much smaller number of iteration steps for convergence than that by the algorithm of Danila et al.     

Building and analyzing the US airport network based on en-route location information

From a complex network perspective, this study sets out two aims around the US airport network (USAN) which is built from en-route location information of domestic flights in the US. First, we analyze the structural properties of the USAN with respect to its binary and weighted graphs, and second we explore the airport patterns, which have wide-ranging implications. Results from the two graphs indicate the following. (1) The USAN exhibits scale-free, small-world and disassortative mixing properties, which are consistent with the mainstream perspectives. Besides, we find (2) a remarkable power relationship between the structural measurements in the binary graph and the traffic measurements in the weighted counterpart, namely degree versus capacity and attraction versus volume. On the other hand, investigation of the airport patterns suggests (3) that all the airports can be classified into four categories based on multiple network metrics, which shows a complete typology of the airports. And it further indicates (4) that there is a subtle relationship between the airport traffic and the geographical constraints as well as the regional socioeconomic indicators.     

The information transmission in community networks

The community structure has been empirically found in many real networks. This paper proposes an efficient Double Shortest Path routing strategy trying to avoid the modules of traffic congestion, which means that we adopt the shortest routing strategy both in the inter-modules and in the intra-module. Simulations show that this routing algorithm is superior to the traditional shortest path routing protocol with appropriate selection of the tunable parameters. In addition, this algorithm can also be improved by integrating it with several alternative routing strategies.     

Detecting community structure in complex networks based on a measure of information discrepancy

Properties of complex networks, such as small-world property, power-law degree distribution, network transitivity, and network- community structure which seem to be common to many real-world networks have attracted great interest among researchers. In this study, global information of the networks is considered by defining the profile of any node based on the shortest paths between it and all the other nodes in the network; then a useful iterative procedure for community detection based on a measure of information discrepancy and the popular modular function Q is presented. The new iterative method does not need any prior knowledge about the community structure and can detect an appropriate number of communities, which can be hub communities or non-hub communities. The computational results of the method on real networks confirm its capability.     

A complex network model for seismicity based on mutual information

Seismicity is the product of the interaction between the different parts of the lithosphere. Here, we model each part of the Earth as a cell that is constantly communicating its state to its environment. As a neuron is stimulated and produces an output, the different parts of the lithosphere are constantly stimulated by both other cells and the ductile part of the lithosphere, and produce an output in the form of a stress transfer or an earthquake. This output depends on the properties of each part of the Earth’s crust and the magnitude of the inputs. In this study, we propose an approach to the quantification of this communication, with the aid of the Information Theory, and model seismicity as a Complex Network. We have used data from California, and this new approach gives a better understanding of the processes involved in the formation of seismic patterns in that region.     

A novel routing scheme in OBS network with sparse wavelength conversion capabilities

The overall burst loss probability is the primary metric of interest in an optical burst switching network with sparse wavelength conversion capabilities (SWCC-OBS). With the overall burst loss probability as the optimization objective, one has to solve an integer nonlinear programming (INLP) problem. In order to overcome the computational difficulties we propose a fictitious play method to approximately solve the INLP. Consequently, a randomized routing strategy can be achieved. The simulation results show that the proposed strategy can give a near-optimal route that avoids the conflict of burst data effectively and decreases the overall burst loss probability. At the same time it also performs well in balancing loads throughout the whole network under different kinds of burst traffic pattern.     

Improved routing strategy based on gravitational field theory

Routing and path selection are crucial for many communication and logistic applications. We study the interaction between nodes and packets and establish a simple model for describing the attraction of the node to the packet in transmission process by using the gravitational field theory, considering the real and potential congestion of the nodes. On the basis of this model, we propose a gravitational field routing strategy that considers the attractions of all of the nodes on the travel path to the packet. In order to illustrate the efficiency of proposed routing algorithm, we introduce the order parameter to measure the throughput of the network by the critical value of phase transition from a free flow phase to a congested phase,and study the distribution of betweenness centrality and traffic jam. Simulations show that, compared with the shortest path routing strategy, the gravitational field routing strategy considerably enhances the throughput of the network and balances the traffic load, and nearly all of the nodes are used efficiently.     

基于引力场理论的复杂网络路由选择策略研究

利用引力场理论对网络传输过程中节点激发的引力场进行了描述,建立了节点的引力场方程,引入α和γ两个参数,用于调节数据传输对节点畅通程度、节点传输能力和路径长度的依赖程度．基于节点的引力场,提出了一种高效的路由选择算法,该算法下数据包将沿着所受路径引力最大的方向进行传递．为检验算法的有效性,引入有序状态参数卵,利用其由自由流到拥塞态的指标流量相变值度量网络的吞吐量,并通过节点的介中心值B分析网络的传输性能和拥塞分布．针对算法在不同α,γ取值条件下的路由情况进行了仿真．仿真结果显示,与传统最短路由算法相比,本文算法将网络传输能力提高了数倍,有效地均衡了节点的介中心值分布,传输路径平均长度（Lavg）随负载量R的增加表现出先增后减的变化趋势,而参数α与γ值的变化对网络传输能力几乎没有影响,说明本文路由算法的性能不依赖于α与γ,对于可行域内任意的α与γ算法都能保证网络传输能力近似相等．     

Community detection based on modularity and an improved genetic algorithm

Complex networks are widely applied in every aspect of human society, and community detection is a research hotspot in complex networks. Many algorithms use modularity as the objective function, which can simplify the algorithm. In this paper, a community detection method based on modularity and an improved genetic algorithm (MIGA) is put forward. MIGA takes the modularity Q$Q$ as the objective function, which can simplify the algorithm, and uses prior information (the number of community structures), which makes the algorithm more targeted and improves the stability and accuracy of community detection. Meanwhile, MIGA takes the simulated annealing method as the local search method, which can improve the ability of local search by adjusting the parameters. Compared with the state-of-art algorithms, simulation results on computer-generated and four real-world networks reflect the effectiveness of MIGA.     

Comparison and performance of routing protocols in SONET based networks

In this paper, comparison of various protocols used in the SONET based networks has been investigated in terms of the performance characteristics like end-to-end queuing delay, end-to-end link throughput, link utilization, etc. By creating a model to simulate the packet flow within a SONET DCC network characteristics of two routing protocols, the RIP (routing information protocol) and OSPF (open shortest path first) defined in the G.7712 standard are analyzed. Finally on comparing all the performance characteristics of the two routing protocol it is observed that OSPF protocol achieves better performance and is the least obtrusive on network operations.     

Robustness analysis of static routing on networks

Robustness is one of the crucial properties that needs to be considered in the design of routing strategies on networks. We study the robustness of three typical routing strategies, which are the SP (shortest path), EP (efficient path), and OP (optimal path) strategies, by simulating several different kinds of attacks including random attacks, target attacks and cascading failures on scale-free networks. Results of the average path length, betweenness centrality, network capacity, etc., demonstrate that the EP strategy is more robust than the other two, and the OP strategy is more reliable than the SP strategy in general. However, on the power-grid network, the OP strategy is more resistant against cascading failures than the EP and SP strategies.     

Delay-constrained anycast routing problem in WDM networks with wavelength conversions

Anycast routing and wavelength assignment problem has been intensively studied in wavelength-division multiplexing (WDM) networks, however, real-time application where packets have explicit expiration deadlines is not taken into consideration. In a WDM network, each link is associated with limited wavelengths available for channel connections. Data transmission over one wavelength to another requires wavelength conversion, which causes a long delay of message transmission. When the network and the set of anycast routing requests are given, the delay-constrained wavelength conversions anycast routing (DWCAR) problem is to find a set of light-paths, one for each source, for anycasting message to one member of the anycast destination group. The objective is to find a minimum number of wavelength conversions with the constraint that the sum of delays along every path must not exceed a given delay-bound. In this paper, we present a Tabu-search based delay-constrained anycast routing algorithm (TSDA) to solve this NP-complete problem, and compare it to a simple K shortest path based anycast algorithm (KSPA). Simulation results show TSDA achieves an average 40–50% improvement in required wavelength conversions.     

Survivable routing and spectrum allocation algorithm based on p-cycle protection in elastic optical networks

With the number of large capacity applications in core network increasing, the bandwidth requirement of optical connections in conventional Wavelength Division Multiplexing (WDM) networks keeps enhancing, so that the Orthogonal Frequency Division Multiplexing (OFDM) technology is adopted to provide higher spectrum efficiency and flexibility in the future elastic optical networks. Meanwhile, survivability in the conventional WDM optical networks has been widely studied as an important issue to ensure the service continuity. However, survivability in OFDM-based elastic optical networks is more challenging than that in conventional WDM optical networks because each fiber usually carries even more connections. Therefore, it is necessary to study the new lightpath protection algorithm in elastic optical networks. Since p-cycle protection scheme has short restoration time and simple protection switching procedure, in this paper, we study the static Survivable p-Cycle Routing and Spectrum Allocation (SC-RSA) problem with providing an Integer Linear Programming (ILP) formulation. Since RSA is a NP-hard problem, we propose a new heuristic algorithm called Elastic p-Cycle Protection (ECP) to tolerate the single-fiber link failure. For each demand, ECP scheme can compute highly-efficient p-cycles to provide protection for all of the on-cycle links and the straddling links. We also consider the load balancing and choose the proper working path for each demand. Simulation results show that the proposed ECP scheme achieves better performances than traditional single-line-rate survivable schemes.     

Efficient and robust routing on scale-free networks

Information routing is one of the most important problems in large communication networks. In this paper we propose a novel routing strategy in which the optimal paths between all pairs of nodes are chosen according to a cost function that incorporates degrees of nodes in paths. Results on large scale-free networks demonstrate that our routing strategy is more efficient than the shortest path algorithm and the efficient routing strategy proposed by Yan et al. [Phys. Rev. E 73, 046108 (2006)]. Furthermore our routing strategy has strong robustness against cascading failure attacks on networks.     

Efficient routing on small complex networks without buffers

In this paper, we are exploring strategies for the reduction of the congestion in the complex networks. The nodes without buffers are considered, so, if the congestion occurs, the information packets will be dropped. The focus is on the efficient routing. The routing strategies are compared using two generic models, i.e., Barabàsi–Albert scale-free network and scale-free network on lattice, and the academic router networks of the Netherlands and France. We propose a dynamic deflection routing algorithm which automatically extends path of the packet before it arrives at congested node. The simulation results indicate that the dynamic routing strategy can further reduce the number of dropped packets in a combination with the efficient path routing proposed by Yan et al. [5].     

Navigation in large subway networks: An informational approach

The structural properties of the subway network are crucial in effective transportation in cities. This paper presents an information perspective of navigation in four different subway networks: New York City, Paris, Barcelona and Moscow. We addressed our study to investigate what is that makes it complicated to navigate in these kinds of networks and we carried out a comparison between them and their intrinsic constraints. Our methodological approach is based on a set of cost/efficiency indicators which are defined in the complex networks literature. We find that the overall complexity in finding stations measured by the average search information S linearly increases as a function of the network size N. The direct implication of this finding is that from these basic levels of required information, the average value H(k) can be represented as a function of the node degree k. Finally, through analyzing subway networks in space P, we reveal the existing service modularity among subway routes using a rescaled expression of S.     

Routing in scale-free networks based on expanding betweenness centrality

In this paper, an improved routing strategy is proposed for enhancing the traffic capacity of scale-free networks. Instead of using the information of degree and betweenness centrality, the new algorithm is derived on the basis of the expanding betweenness centrality of nodes, which gives an estimate of the traffic handled by the vertex for a certain route set. Since the nodes with large betweenness centrality are more susceptible to traffic congestion, the traffic can be improved by redistributing traffic loads from nodes with large betweenness centrality to nodes with small betweenness centrality in the process of computing the collective routing table. Comparing with results of previous routing strategies, it is shown that the present improved routing performs more effectively.     

Traffic dynamics based on a traffic awareness routing strategy on scale-free networks

By incorporating local traffic information into the shortest path routing strategy, we numerically investigate the effectiveness of the traffic awareness routing strategy for scale-free networks with different clustering. In order to characterize the efficiency of the packet-delivery process, we introduce an order parameter and an average transmission time that allow us to measure the network capacity by the critical value of phase transition from free flow to congestion. Compared with the shortest path routing protocol, the network capacity is greatly enhanced by the traffic awareness routing strategy. We also find that there exists an optimum value for the tunable parameter in the congestion awareness strategy. Moreover, simulation results show that the more clustered the network, the less efficient the packet-delivery process.