18 research outputs found
Effective and Efficient Similarity Index for Link Prediction of Complex Networks
Predictions of missing links of incomplete networks like protein-protein
interaction networks or very likely but not yet existent links in evolutionary
networks like friendship networks in web society can be considered as a
guideline for further experiments or valuable information for web users. In
this paper, we introduce a local path index to estimate the likelihood of the
existence of a link between two nodes. We propose a network model with
controllable density and noise strength in generating links, as well as collect
data of six real networks. Extensive numerical simulations on both modeled
networks and real networks demonstrated the high effectiveness and efficiency
of the local path index compared with two well-known and widely used indices,
the common neighbors and the Katz index. Indeed, the local path index provides
competitively accurate predictions as the Katz index while requires much less
CPU time and memory space, which is therefore a strong candidate for potential
practical applications in data mining of huge-size networks.Comment: 8 pages, 5 figures, 3 table
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