The structure of the network has great impact on its traffic dynamics. Most of the real world networks follow heterogeneous structure and exhibit scale-free feature. In scale-free network, a new node prefer to connect with hub nodes and the network capacity is curtailed by smaller degree nodes. Therefore, we propose rewiring a fraction of links in the network, to improve the network transport efficiency. In this paper, we discuss some efficient link rewiring strategies and perform simulations on scale-free networks, confirming the effectiveness of these strategies. The rewiring strategies actually reduce the centrality of the nodes having higher betweenness centrality. After the link rewiring process, the degree distribution of the network remains the same. This work will be beneficial for the enhancement of network performance.
Community detection algorithms (CDAs) are aiming to group nodes based on their connections and play an essential role in the complex system analysis. However, for privacy reasons, we may want to prevent communities or a group of nodes in the complex industrial network from being discovered in some instances, leading to the topics on community deception. In this paper, we introduce and formalize two intelligent community deception methods to conceal the nodes from various CDAs. We used node‐based matrices, persistence and safeness scores, to formalize the optimization problems to confound the CDAs. The persistence score is used to destabilize the constant communities in the network while the safeness score is used to assess the level of hiding of a node from CDAs. The objective functions aim to minimize the persistence score and maximize the safeness score of the nodes in the network. From the simulation results, it can be analyzed that the proposed strategies are intelligently concealing the community information in the complex industrial system.
Since all the existing real world networks are evolving, the study of traffic dynamics is a challenging task. Avoidance of traffic congestion, system utility maximization and enhancement of network capacity are prominent issues. Network capacity may be improved either by optimizing network topology or enhancing in routing approach. In this context, we propose and design a model of the time-varying data communication networks (TVCN) based on the dynamics of inflowing links. Traffic congestion can be avoided by using a suitable centrality measure, especially betweenness and Eigen vector centralities. If the nodes coming in user’s route are most betweenness central, then that route will be highly congested. Eigen vector centrality is used to find the influence of a node on others. If a node is most influential, then it will be highly congested and considered as least reputed. For that reason, routes are chosen such that the sum of the centralities of the nodes coming in user’s route should be minimum. Furthermore, Kelly’s optimization formulation for a rate allocation problem is used for obtaining optimal rates of distinct users at different time instants and it is found that the user’s path with lowest betweenness centrality and highest reputation will always give maximum rate at the stable point.
Received (Day Month Year) Revised (Day Month Year)The network topology and the routing strategy are major factors to affect the traffic dynamics of the network. In this work, we aim to design an optimal time-varying network structure and an efficient route is allocated to each user in the network. The network topology is designed by considering addition, removal, and rewiring of links. At each time instants, a new node connects with an existing node based on the degree and correlation with its neighbor. Traffic congestion is handled by rewiring of some congested links along with the removal of the anti-preferential and correlated links. Centrality plays an important role to find the most important node in the network. The more a node is central, the more it can be used for the shortest route of the user pairs and it can be congested due to a large number of data coming from its neighborhood. Therefore, routes of the users are selected such that the sum of the centrality of the nodes appearing in the user's route is minimum. Thereafter, we analyze the network structure by using various network properties such as the clustering coefficient, centrality, average shortest path, rich club coefficient, average packet travel time and order parameter.November 21, 2018 1:51 WSPC/INSTRUCTION FILE ws-mplb˙arxiv 2 Authors' Names which is determined by the structure of the underlying network. In this context, an optimal time-varying communication network model is designed to avoid congestion in the network and user's route is selected based on centrality information especially, betweenness centrality.It is found that the communication networks are scale-free (SF) 6 and are more susceptible to traffic congestion than some homogeneous networks. 7 In SF networks large degree nodes posses a large volume of data hence, congestion usually starts at these nodes and then spreads to the whole network. Therefore, researchers proposed various strategies, 8 which can be classified into hard and soft strategies in order to handle traffic congestion and enhance network capacity. The restructuring of network topology comes under hard strategies. Zhao et al. redistributed a load of heavily loaded nodes to others, 5 some connections are removed between large degree nodes, 9 high betweenness centrality nodes are removed first 10 and links are added between the nodes with long distance. 11 Jiang et al., 12 assigned capacity dynamically to each link proportional to the queue length of the link. Some fraction of links is rewired based on node's degree information and betweenness centrality. 4 Chen et al. 13 rewired the link against traffic congestion and proved that the network should have a core-periphery structure.Sometimes it is impossible to modify the network topological structure and it also incurs a high cost to change the structure of the network. Hence, a soft strategy based on finding a better routing strategy is preferable to enhance the network capacity. Yin et al. 14 chose an efficient path (EP) for routing. Zhao et al. 15 assigned di...
All the existing real world networks are evolving, hence, study of traffic dynamics in these enlarged networks is a challenging task. The critical issue is to optimize the network structure to improve network capacity and avoid traffic congestion. We are interested in taking user's routes such that it is least congested with optimal network capacity. Network capacity may be improved either by optimizing network topology or enhancing in routing approach. In this context, we propose and design a model of the time varying data communication networks (TVCN) based on the dynamics of in-flowing links. Newly appeared node prefers to attach with most influential node present in the network. In this paper, influence is termed as reputation and is applied for computing overall congestion at any node. User path with least betweenness centrality and most reputation is preferred for routing. Kelly's optimization formulation for a rate allocation problem is used for obtaining optimal rates of distinct users at different time instants and it is found that the user's path with lowest betweenness centrality and highest reputation will always give maximum rate at stable point.
Modern real-world infrastructure systems consist of a coupled and interdependent subsystems. These system can be modelled as interdependent multilayer networks such as communication networks, power networks, transport networks, etc. These networks are prone to cascade failure of intra-layer and inter-layer links due to overload. For example, in power distribution networks, the flow of electric current beyond their capacities may cause to fail the distribution lines. In multilayer network systems, any damage to the links (intra-layer and inter-layer) may cause cascade failure due to redistribution of loads of failed links to the live links. If it is not controlled, it may damage the entire system. However, optimally redistribution of loads of the failed links to live links can minimize the damage of live links due to overload. In this work, we propose a method to optimally redistribute loads of failed links (intra-layer and inter-layer) to the live links in the interdependent multilayer network in the event of cascade failure. For this purpose, we consider three variants of synthetic and two empirical dataset multilayer networks. Our simulation results reveal that optimal redistribution of loads reduces the number of failed links. Besides, it also reduces the amount of extra load (due to failed links) to be redistributed on the live links. It leads to enhance the number of live links and maintaining the robustness of the entire multilayer networks.
Communication networks are time-varying and hence, fair sharing of network resources among the users in such dynamic environment is a challenging task. In this context, a time-varying network model is designed and shortest user's route is found. In the designed network model, an end to end window-based congestion control scheme is developed with the help of internal nodes or router and the end user can get implicit feedback (RTT and throughput). This scheme is considered as fair if the allocation of resources among users minimizes overall congestion or backlog in the networks. Window update approach is based on multi-class fluid model and is updated dynamically by considering delays (communication, propagation and queuing) and the backlog of packets in the user's routes. Convergence and stability of the window size are obtained using a Lyapunov function. A comparative study with other window-based methods is also provided. IntroductionReal life systems such as communication, social, biological can be described with the help of complex networks [1,2]. It is a challenging task to design a time-varying communication network (TVCN) with the ability to respond to the randomly changing traffic. Hence, the study of the traffic dynamics and fair sharing of resources on communication networks has received a great wave of interest for the researchers in past few years. The allocation of resources among the users in an unbiased manner is one of the challenging tasks in today's scenario. For assigning resources in an unbiased or fair manner, some researchers use various rate vector allocation schemes [3,4] to gain maximum utility while others select fair end to end window-based congestion control scheme [5,4]. In this paper, we are interested to design a TVCN model considering network growth, redistribution of traffic from heavily loaded nodes and removal of some fraction of links to reduce maintenance cost. A window-based congestion control scheme may be applied on the proposed TVCN model and user's current window size may be updated by considering delays(communication, propagation and queuing delays).Communication networks are evolving and the concept of evolving networks with preferential linking during the addition of new nodes is introduced by Barabasi-Albert ([6]). The distribution of degrees of nodes in these networks follow the power law and is termed as scale free nature of the networks. Many time-varying graph (TVG) models are proposed [7,8]. A series of static graphs (i.e., the snapshots) is used to represent the network at a given time instant [9]. Wehmuth et al. [7] proposed a new unifying model for representing finite discrete TVGs. A framework is designed to obtain degree distribution of evolving network with the consideration of deletion of nodes and a continuum formulation is also provided by [10]. A preferential attachment model for network growth is proposed where a new node has partial information about the network [11]. A new node has access to a fraction of nodes and a new connection is formed with the...
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