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arXiv · 1206.2199

Predicting link directions via a recursive subgraph-based ranking

Fangjian Guo·Zimo Yang·Tao Zhou
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Abstract

Link directions are essential to the functionality of networks and their prediction is helpful towards a better knowledge of directed networks from incomplete real-world data. We study the problem of predicting the directions of some links by using the existence and directions of the rest of links. We propose a solution by first ranking nodes in a specific order and then predicting each link as stemming from a lower-ranked node towards a higher-ranked one. The proposed ranking method works recursively by utilizing local indicators on multiple scales, each corresponding to a subgraph extracted from the original network. Experiments on real networks show that the directions of a substantial fraction of links can be correctly recovered by our method, which outperforms either purely local or global methods.

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Fangjian Guo, Zimo Yang, Tao Zhou. 2012-06-11. Predicting link directions via a recursive subgraph-based ranking. https://doi.org/10.1016/j.physa.2013.03.025

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