Predicting Missing Links via Local Information

Missing link prediction of networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely common neighbors, has the best overall performance, and the Adamic-Adar index performs the second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbors. It is found that many links are assigned same scores if only the information of the nearest neighbors is used. We therefore design another new measure exploited information of the next nearest neighbors, which can remarkably enhance the prediction accuracy.Comment: For International Workshop: "The Physics Approach To Risk: Agent-Based Models and Networks", http://intern.sg.ethz.ch/cost-p10

Routing-Aware Time Slot Allocation Heuristics in Contention-Free Sensor Networks

Part 7: Wireless Sensor NetworksInternational audienceTraditionally, in Wireless Sensor Networks, protocols are designed independently in the layered protocol stack, and metrics involved in several layers can be affected. Communication latency is one metric example, impacted by both the routing protocol in the network layer and the MAC protocol in the data link layer. Better performances can be obtained using cross-layer approaches.In this paper, we address latency optimizations for communications in sensor networks, based on cross-layer decisions. More particularly, we propose new time slot scheduling methods correlated to routing decisions. Slot allocation for nodes follows particular routing tree traversals, trying to reduce the gap between the slot of a child and that of its parent.Simulations show that latency performance of our contributions improves similar cross-layer approaches from 33 % up to 54 %. Duty cycle of obtained schedules are also improved from 7 % up to 11 %

Link prediction in complex networks: a clustering perspective

Link prediction is an open problem in the complex network, which attracts much research interest currently. However, little attention has been paid to the relation between network structure and the performance of prediction methods. In order to fill this vital gap, we try to understand how the network structure affects the performance of link prediction methods in the view of clustering. Our experiments on both synthetic and real-world networks show that as the clustering grows, the precision of these methods could be improved remarkably, while for the sparse and weakly clustered network, they perform poorly. We explain this through the distinguishment caused by increased clustering between the score distribution of positive and negative instances. Our finding also sheds light on the problem of how to select appropriate approaches for different networks with various densities and clusterings.Comment: 7 pages, 3 figure