  Inspired by traditional link prediction and to solve the problem of
recommending friends in social networks, we introduce the personalized link
prediction in this paper, in which each individual will get equal number of
diversiform predictions. While the performances of many classical algorithms
are not satisfactory under this framework, thus new algorithms are in urgent
need. Motivated by previous researches in other fields, we generalize heat
conduction process to the framework of personalized link prediction and find
that this method outperforms many classical similarity-based algorithms,
especially in the performance of diversity. In addition, we demonstrate that
adding one ground node who is supposed to connect all the nodes in the system
will greatly benefit the performance of heat conduction. Finally, better hybrid
algorithms composed of local random walk and heat conduction have been
proposed. Numerical results show that the hybrid algorithms can outperform
other algorithms simultaneously in all four adopted metrics: AUC, precision,
recall and hamming distance. In a word, this work may shed some light on the
in-depth understanding of the effect of physical processes in personalized link
prediction.
