The Algorithm of Link Prediction on Social Network

Abstract EN

At present, most link prediction algorithms are based on the similarity between two entities.

Social network topology information is one of the main sources to design the similarity function between entities.

But the existing link prediction algorithms do not apply the network topology information sufficiently.

For lack of traditional link prediction algorithms, we propose two improved algorithms: CNGF algorithm based on local information and KatzGF algorithm based on global information network.

For the defect of the stationary of social network, we also provide the link prediction algorithm based on nodes multiple attributes information.

Finally, we verified these algorithms on DBLP data set, and the experimental results show that the performance of the improved algorithm is superior to that of the traditional link prediction algorithm.