Measure The Robustness Of Link Prediction Algorithms Under The Noisy Environment

Link prediction in complex networks is to estimate the likelihood of two nodes to interact with each other in the future.As this problem has applications in a large number of real systems,many link prediction methods have been proposed.However,the validation of these methods is so far mainly conducted in the assumed noise-free networks.Therefore,we still miss a clear understanding of how the prediction results would be affected if the observed network data is no longer accurate.In this paper,we comprehensively study the robustness of the existing link prediction algorithms in the real networks where some links are missing,fake or swapped with other links.We find that missing links are more destructive than fake and swapped links for prediction accuracy.An index is proposed to quantify the robustness of the link prediction methods.Among the twenty-two studied link prediction methods,we find that though some methods have low prediction accuracy,they tend to perform reliably in the "noisy" environment.In this paper,we study the performance of the proposed algorithm in the symbol network,and design a new recommendation algorithm evaluation index.In order to evaluate the massive recommendation algorithm,the researchers designed a large number of recommendation algorithm evaluation index.Many researches focus on the accuracy of the evaluation algorithm,and the accuracy of the algorithm is:AUC,Precision,Recall,AUPR,MAE,RMSE,etc..Because of the different purposes of evaluation,there are still some work to study the accuracy of the impact of user satisfaction index,these indicators include:coverage,product popularity,etc..But the value of each recommendation is ignored in these evaluation methods.For the symbol of the user-item network,the symbol information is an important part of the network,and the evaluation method which is proposed by the former is ignored.In this work,we study the recommendation results in different symbols on the edge of the distribution,find that some algorithm of recommendation results in bias in the recommended negative to the information to the user,this algorithm in practice would seriously reduce the user's perception of recommender system.Then,we design a recommendation algorithm to consider the information of the edge of the edge of the evaluation index,and analyze the performance of the 4 common recommendation algorithm.