Research on Complex Network Link Prediction Combining Node Similarity and Community Information
As a challenging research direction in complex networks,link prediction has a very broad application prospect.Link prediction is called the prediction of missing or unobserved links in the network.The most cutting-edge link prediction meth-ods either only consider the similarity between nodes or simply mine the information between communities,and they don't achieve very good forecast purposes.In order to solve the above problems,this paper proposes a metric to measure the strength of the rela-tionship between overlapping communities and non-overlapping communities,and in order to better consider the impact of informa-tion between communities on predictions,a new community division method is also introduced,and finally a link prediction frame-work that considers node similarity and community structure information is proposed at the same time.Experimental results show that compared with the existing algorithms,the link prediction algorithm proposed in this paper improves the AUC accuracy by 0.2～10.59 percentage points,which proves that the method proposed in this paper is effective.
complex networklink predictionnode similaritystrength of community relationscommunity division
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