Predicting link directions via a recursive subgraph-based ranking by Fangjian Guo, Zimo Yang, and Tao Zhou.
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.
This paper focuses mostly on prediction of direction of links, relying on other research for the question of link existence.
I mention it because predicting links and their directions will be important for planning graph database deployments in particular.
It will be a little late to find out when under full load that other modeling choices should have been made. (It is usually under “full load” conditions when retrospectives on modeling choices come up.)