Cross-Domain Scientific Collaborations Prediction with Citation Information

Cross-domain Scientific Collaborations have promoted rapid development of science and generated many innovative breakthroughs. However, predicting cross-domain scientific collaboration problem is rarely studied and collaboration recommendation methods within single domain cannot be directly utilized for solving cross-domain problems. In this paper, we propose a Hybrid Graph Model, which combines both explicit co-author relationships and implicit co-citation relationships together to construct a hybrid graph and then Random Walks with Restarts concept is used to measure and rank relatedness. The experiments with large publication data set show that Hybrid Graph Model outperforms some baseline approaches on several recommendation metrics. Citation information has been demonstrated to be very helpful for scientific collaboration recommendations as well.