Abstract:
Traditional metrics of node influence such as degree or betweenness identify highly influential nodes, but are rarely usefully accurate in quantifying the spreading power of nodes which are not. Such nodes are the vast majority of the network, and the most likely entry points for novel influences, be they pandemic disease or new ideas. Several recent works have suggested metrics based on path counting. The current work proposes instead using the expected number of infected-susceptible edges, and shows that this measure predicts spreading power in discrete time, continuous time, and competitive spreading processes simulated on large random networks and on real world networks. Applied to the Ugandan road network, it predicts that Ebola is unlikely to pose a pandemic threat.

Abstract:
Identifying the node spreading influence in networks is an important task to optimally use the network structure and ensure the more efficient spreading in information. In this paper, by taking into account the shortest distance between a target node and the node set with the highest $k$-core value, we present an improved method to generate the ranking list to evaluate the node spreading influence. Comparing with the epidemic process results for four real networks and the Barab\'{a}si-Albert network, the parameterless method could identify the node spreading influence more accurately than the ones generated by the degree $k$, closeness centrality, $k$-shell and mixed degree decomposition methods. This work would be helpful for deeply understanding the node importance of a network.

Abstract:
In order to evaluate the node importance in complex network, considering the disadvantages of node deletion method, node contraction method and betweenness method, through defining the node efficiency and the node importance evaluation matrix, a method to find the vital node in complex networks is proposed by using the node importance evaluation matrix. Considered in this method are the node efficiency, node degree and adjacent node importance contributions, and used adjacent node degree and efficiency value to characterize the contribution of their importance. Finally, an optimized algorithm whose time complexity was O(Rn2) is provided. Experiments show that this method is effective and feasible, and it is applicable to large scale complex networks.

Abstract:
This Paper describes a good algorithm to find theFaulty node in any given complex Network and provides thealternative path and also tells us the number of faulty node. As theTechnological advances increasing number of node day by day ina Network. If a node fails, the system continues to operate withdegraded performance until the faulty node is repaired. If therepair operation will take an unacceptable amount of time, it isuseful to replace the faulty node with a spare node. However, theappropriate procedures must be followed and precautions must betaken so you do not interrupt I/O operations and compromise theintegrity of your data that we have presented in our paper.

Abstract:
The Identification of the influential nodes in networks is one of the most promising domains. In this paper, we present an improved iterative resource allocation (IIRA) method by considering the centrality information of neighbors and the influence of spreading rate for a target node. Comparing with the results of the Susceptible Infected Recovered (SIR) model for four real networks, the IIRA method could identify influential nodes more accurately than the tradition IRA method. Specially, in the Erdos network, the Kendall's tau could be enhanced 23\% when the spreading rate is 0.12. In the Protein network, the Kendall's tau could be enhanced 24\% when the spreading rate is 0.08.

Abstract:
We study a dynamical model of epidemic spreading on complex networks in which there are explicit correlations among the node's connectivities. For the case of Markovian complex networks, showing only correlations between pairs of nodes, we find an epidemic threshold inversely proportional to the largest eigenvalue of the connectivity matrix that gives the average number of links that from a node with connectivity $k$ go to nodes with connectivity $k'$. Numerical simulations on a correlated growing network model provide support for our conclusions.

Abstract:
Insulators can block the action of enhancers on promoters and the spreading of repressive chromatin, as well as facilitating specific enhancer-promoter interactions. However, recent studies have called into question whether the activities ascribed to insulators in model transgene assays actually reflect their functions in the genome. The Drosophila even skipped (eve) gene is a Polycomb (Pc) domain with a Pc-group response element (PRE) at one end, flanked by an insulator, an arrangement also seen in other genes. Here, we show that this insulator has three major functions. It blocks the spreading of the eve Pc domain, preventing repression of the adjacent gene, TER94. It prevents activation of TER94 by eve regulatory DNA. It also facilitates normal eve expression. When Homie is deleted in the context of a large transgene that mimics both eve and TER94 regulation, TER94 is repressed. This repression depends on the eve PRE. Ubiquitous TER94 expression is “replaced” by expression in an eve pattern when Homie is deleted, and this effect is reversed when the PRE is also removed. Repression of TER94 is attributable to spreading of the eve Pc domain into the TER94 locus, accompanied by an increase in histone H3 trimethylation at lysine 27. Other PREs can functionally replace the eve PRE, and other insulators can block PRE-dependent repression in this context. The full activity of the eve promoter is also dependent on Homie, and other insulators can promote normal eve enhancer-promoter communication. Our data suggest that this is not due to preventing promoter competition, but is likely the result of the insulator organizing a chromosomal conformation favorable to normal enhancer-promoter interactions. Thus, insulator activities in a native context include enhancer blocking and enhancer-promoter facilitation, as well as preventing the spread of repressive chromatin.

Abstract:
We investigate the spread of diseases, computer viruses or information on complex networks and also immunization strategies to prevent or control the spread. When an entire population cannot be immunized and the effect of immunization is not perfect, we need the targeted immunization with immunization rate. Under such a circumstance we calculate epidemic thresholds for the SIR and SIS epidemic models. It is shown that, in scale-free networks, the targeted immunization is effective only if the immunization rate is equal to one. We analyze here epidemic spreading on directed complex networks, but similar results are also valid for undirected ones.

Abstract:
As an important type of dynamics on complex networks, spreading is widely used to model many real processes such as the epidemic contagion and information propagation. One of the most significant research questions in spreading is to rank the spreading ability of nodes in the network. To this end, substantial effort has been made and a variety of effective methods have been proposed. These methods usually define the spreading ability of a node as the number of finally infected nodes given that the spreading is initialized from the node. However, in many real cases such as advertising and medicine science the spreading only aims to cover a specific group of nodes. Therefore, it is necessary to study the spreading ability of nodes towards localized targets in complex networks. In this paper, we propose a reversed local path algorithm for this problem. Simulation results show that our method outperforms the existing methods in identifying the influential nodes with respect to these localized targets. Moreover, the influential spreaders identified by our method can effectively avoid infecting the non-target nodes in the spreading process.

Abstract:
A Wireless Sensor Network is a set of multiple connected components. Sometimes due to the failure of some of its nodes, the sensor network communication fails. So that we consider this problem of node(s) failure termed as “cut” from the remaining nodes of a wireless sensor network. We propose an algorithm that allows (i) every node to detect when the connectivity to a specially designated node has been lost, and (ii) one or more nodes (that are connected to the special node after the cut) to detect the occurrence of the cut. The algorithm we proposed is distributed and asynchronous i.e. every node needs to communicate with only those nodes that are within its communication range. The algorithm is based on the iterative computation of the nodes. The convergence rate of the underlying iterative scheme is independent of the size and structure of the network. In this algorithm we devised a way to solve the problem of redundant information at the destination which arises due to availability of information at every node that is initially sent from the source node. We demonstrate the effectiveness of the proposed algorithm through simulation.