Mitigate SIR epidemic spreading via contact blocking in temporal networks

Progress has been made in how to suppress epidemic spreading on temporal networks via blocking all contacts of targeted nodes or node pairs. In this work, we develop contact blocking strategies that remove a fraction of contacts from a temporal (time evolving) human contact network to mitigate the spread of a Susceptible-Infected-Recovered epidemic. We define the probability that a contact c(i, j, t) is removed as a function of a given centrality metric of the corresponding link l(i, j) in the aggregated network and the time t of the contact. The aggregated network captures the number of contacts between each node pair. A set of 12 link centrality metrics have been proposed and each centrality metric leads to a unique contact removal strategy. These strategies together with a baseline strategy (random removal) are evaluated in empirical contact networks via the average prevalence, the peak prevalence and the time to reach the peak prevalence. We find that the epidemic spreading can be mitigated the best when contacts between node pairs that have fewer contacts and early contacts are more likely to be removed. A strategy tends to perform better when the average number contacts removed from each node pair varies less. The aggregated pruned network resulted from the best contact removal strategy tends to have a large largest eigenvalue, a large modularity and probably a small largest connected component size.

A supervised similarity measure for link prediction based on KNN

As a research hotspot of complex network analysis, link prediction has received growing attention from various disciplines. Link prediction intends to determine the connecting probability of latent links based on the observed structure information. To this end, a host of similarity-based and learning-based link prediction methods have been proposed. To attain stable prediction performance on diverse networks, this paper proposes a supervised similarity-based method, which absorbs the advantages of both kinds of link prediction methods. In the proposed method, to capture the characteristics of a node pair, a collection of structural features is extracted from the network to represent the node pair as a vector. Then, the positive and negative [Formula: see text]-nearest neighbors are searched from existing and nonexisting links, respectively. The connection likelihood of a node pair is measured according to its distances to the local mean vectors of positive and negative [Formula: see text]-nearest neighbors. The prediction performance of the proposed method is experimentally evaluated on 10 benchmark networks. The results show that the proposed method is superior to the compared methods in terms of accuracy and stableness.

An Algorithm Based on Loop Cutting Contribution Function for Loop Cutset Problem in Bayesian Network

The loop cutset solving algorithm in the Bayesian network is particularly important for Bayesian inference. This paper proposes an algorithm for solving the approximate minimum loop cutset based on the loop cutting contribution index. Compared with the existing algorithms, the algorithm uses the loop cutting contribution index of nodes and node-pairs to analyze nodes from a global perspective, and select loop cutset candidates with node-pair as the unit. The algorithm uses the parameter μ to control the range of node pairs, and the parameter ω to control the selection conditions of the node pairs, so that the algorithm can adjust the parameters according to the size of the Bayesian networks, which ensures computational efficiency. The numerical experiments show that the calculation efficiency of the algorithm is significantly improved when it is consistent with the accuracy of the existing algorithm; the experiments also studied the influence of parameter settings on calculation efficiency using trend analysis and two-way analysis of variance. The loop cutset solving algorithm based on the loop cutting contribution index uses the node-pair as the unit to solve the loop cutset, which helps to improve the efficiency of Bayesian inference and Bayesian network structure analysis.

Accelerating DNA Computing via PLP-qPCR Answer Read out to Solve Traveling Salesman Problems

An asymmetric, fully-connected 8-city traveling salesman problem (TSP) was solved by DNA computing using the ordered node pair abundance (ONPA) approach through the use of pair ligation probe quantitative real time polymerase chain reaction (PLP-qPCR). The validity of using ONPA to derive the optimal answer was confirmed by in silico computing using a reverse-engineering method to reconstruct the complete tours in the feasible answer set from the measured ONPA. The high specificity of the sequence-tagged hybridization, and ligation that results from the use of PLPs significantly increased the accuracy of answer determination in DNA computing. When combined with the high throughput efficiency of qPCR, the time required to identify the optimal answer to the TSP was reduced from days to 25 min.

A potential energy and mutual information based link prediction approach for bipartite networks

Link prediction in networks has applications in computer science, graph theory, biology, economics, etc. Link prediction is a very well studied problem. Out of all the different versions, link prediction for unipartite graphs has attracted most attention. In this work we focus on link prediction for bipartite graphs that is based on two very important concepts—potential energy and mutual information. In the three step approach; first the bipartite graph is converted into a unipartite graph with the help of a weighted projection, next the potential energy and mutual information between each node pair in the projected graph is computed. Finally, we present Potential Energy-Mutual Information based similarity metric which helps in prediction of potential links. To evaluate the performance of the proposed algorithm four similarity metrics, namely AUC, Precision, Prediction-power and Precision@K were calculated and compared with eleven baseline algorithms. The Experimental results show that the proposed method outperforms the baseline algorithms.

Evaluating on Topology Survivability Based on Largest Number of Node-Disjoint Paths

<p>On the point of view of Largest Number of Node-Disjoint Path (LNNDP for short) between a node pair in a network, this article states the importance of LNNDP to global survivability of topology at first, then proposes an algorithm to compute maximal number of node-disjoint paths between node pairs. A new topology survivability metric based on LNNDP is put forward to evaluate the global survivability of network topology. It can be used to evaluate the survivability of topology provided. This metric can express accurately global topology survivability.</p>

SEGIN-Minus: A New Approach to Design Reliable and Fault-Tolerant MIN

Background: VLSI technology advancements have resulted the requirements of high computational power, which can be achieved by implementing multiple processors in parallel. These multiple processors have to communicate with their memory modules by using Interconnection Networks (IN). Multistage Interconnection Networks (MIN) are used as IN, as they provide efficient computing with low cost. Objective: the objective of the study is to introduce new reliable MIN named as a (Shuffle Exchange Gamma Interconnection Network Minus) SEGIN-Minus, which provide reliability and faulttolerance with less number of stages. Methods: MUX at input terminal and DEMUX at output terminal of SEGIN has been employed with reduction in one intermidiate stage. Fault tolerance has been introduced in the form of disjoint paths formed between each source-destnation node pair. Hence reliability has been improved. Results: Terminal, Broadcast and Network Reliability has been evaluated by using Reliability Block Diagrams for each source-destination node pair. The results have been shown, which depicts the hiher reliability values for newly proposed network. The cost analysis shows that new SEGINMinus is a cheaper network than SEGIN. Conclusion: SEGIN-Minus has better reliability and Fault-tolerance than priviously proposed SEGIN.