Implementation of Network Data Mining Algorithm for Associated Users Based on Multi-Information Fusion

Abstract

In order to accurately and effectively mine relevant users in social networks, we can stop false information and illegal activities in the network, thereby ensuring the safety and integrity of the network environment. A method is proposed for the implementation of a data mining algorithm of a user network based on the fusion of several data. AUMA-MRL (associated user mining algorithm based on multi-information representation learning) proposes an associated user mining algorithm based on node characteristics, neighborhood information, and global network structure information. The steps of the algorithm are as follows: combining each user of the social network into a node using a method where each node is installed separately and combining network user characteristics and user relationship information. A user pair is a network similarity vector that represents the similarity of users in different dimensions. Based on these similarity vectors, a corresponding user separation algorithm is formed. It examines the feasibility and efficiency of the AUMA-MRL algorithm for researching relevant users. The proportion of associated users in the network to be fused is lower than that of nonassociated users, and the prediction has little effect on improving the recall rate of nonassociated users, so the recall rate is slightly lower than the accuracy. This algorithm can quickly get the embedding of new nodes and the similarity vector between new nodes and other nodes in the network, so as to quickly mine the associated users of new nodes in the network and enhance the robustness of the network associated user mining algorithm.