3 research outputs found
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
Identifying Missing and Spurious Interactions in Directed Networks
Recent years, the studies of link prediction have been overwhelmingly emphasizing on undirected networks. Compared with it, how to identify missing and spurious interactions in directed networks has received less attention and still is not well understood. In this paper, we make use of classical link prediction indices for undirected networks, adapt them to directed version which could predict both the existence and direction of an arc between two nodes, and investigate their prediction ability on six real-world directed networks. Experimental results demonstrate that those modified indices perform quite well in directed networks. Compared with bifan predictor, some of them can provide more accurate predictions