Research On Method Of Complex Network Link Prediction Based On Reciprocal Link

Complex networks are effective tools for modeling and analyzing complex systems and play a key role in understanding complex behaviors.Link prediction is a method of predicting future or unknown connections in complex networks.At present,most research on link prediction focuses on link prediction in the field of undirected networks.However,the edges in real networks are often directed.Simply using undirected network indicators on directed networks will reduce the prediction accuracy.Aiming at the problem that the link prediction of directed networks only considers a single reciprocal link structure,but ignores other topological factors of the node in the network,resulting in low prediction accuracy,a link prediction algorithm based on the weighted reciprocal link count and a link prediction algorithm based on the reciprocal link count are proposed.The main research content of the indirect reciprocity weighting algorithm of node contribution is as follows.First of all,in view of the problem of potential theory ignoring the structure of subgraphs containing reciprocal edges,a reciprocal growth model is proposed,which screens the directed closed triples with different numbers of reciprocal links in the directed network,and finds that there are There are a lot of reciprocal links.In view of the problem that the current reciprocal link weighting algorithm does not consider the node degree resource,the reciprocal link count index is introduced as the weight of the directed edge in the directed closed triplet,and the node degree resource is comprehensively considered,and a chain weighted by the reciprocal link count is proposed.Road prediction algorithm.Secondly,in view of the problem that the current reciprocal link weighting algorithm only considers the factor of the reciprocal coefficient to reduce the prediction accuracy,this paper introduces the contribution of each node in the network,and then comprehensively considers the important role of the reciprocal link,and integrates the local node contribution and the global The reciprocity coefficient of,transforms the reciprocal link information into the weight between the node pairs,and proposes a new weighting mechanism,that is,the indirect reciprocity weighting algorithm based on node contribution.Finally,the link prediction algorithm based on reciprocal link count weighting and the indirect reciprocal weighting algorithm based on node contribution are tested and verified on different types of real data sets,and compared and analyzed with classic weighting indicators to verify that this paper proposes the effectiveness of the algorithm.