Multi-Resolution Prediction Model Based on Community Relevance for Missing Links Prediction

The existing research demonstrates that the link prediction algorithm which based on community similarity has better prediction performance than that of other node similarity-based methods, and it is more suitable for predicting the probability of the missing links between node-pairs with far distance. However, the disadvantage of these community similarity-based methods is the resolution of prediction accuracy is very low, which resulting in the existence probability of the missing links between node-pairs within a community or between a specific pair of communities is the same. In addition, the link prediction algorithms which based on multi-resolution community division can calculate the existence probability of missing links under different resolutions, but the relevance between communities had not taken into account, which makes it difficult to predict the existence probability of target links if the number of interconnections between communities is small. Combining the advantages of these two algorithms, we propose a more realistic link prediction model which based on a novel quasi-local community relevance index under multi-resolution community division. The performance of our algorithms is demonstrated by comparing with other well-known methods on two kinds of networks in different scales. The experiment results indicate that our approaches are very competitive.