Research on Link Prediction Algorithms Based on Multichannel Structure Modelling

Today’s link prediction methods are based on the network structure using a single-channel approach for prediction,and there is a lack of link prediction algorithms constructed from a multichannel approach,which makes the features monotonous and noncomplementary.To address this problem,this paper proposes a link prediction algorithm based on multichannel structure modelling(MCLP).First,the network is sampled three times to construct its three subgraph structures.Second,the node representation vectors of the network are learned separately for each subgraph on a single channel.Then,the three node representation vectors are combined,and the similarity matrix is calculated for the combined vectors.Finally,the performance of the MCLP algorithm is evaluated by calculating the AUC using the similarity matrix and conducting multiple experiments on three citation network datasets.The experimental results show that the proposed link prediction algorithm has an AUC of 98.92%,which is better than the performance of the 24 link prediction comparison algorithms used in this paper.The experimental results sufficiently prove that the MCLP algorithm can effectively extract the relationships between network nodes,and confirm its effectiveness and feasibility.