Federated learning enabled graph convolutional autoencoder and factorization machine for potential friendship prediction in social networks

Abstract

Friendships are the keystone of social networks. Predicting potential friendships of users in social networks has become a critical task in the real world. However, the computational models proposed by previous researchers do not effectively capture the behavior preferences of users, which limits the recommendation results. Moreover, the process of generating predicted values has not considered the cross-information among different features. Therefore, we design a potential friendship prediction model based on graph convolutional autoencoder and factorization machine (GCAFM). The GCAFM model uses a graph convolutional autoencoder to learn the hidden feature of users, which can make the similar users as close as possible in the embedding space. The hidden feature of the target users is then subjected to an element-to-element multiplication operation, and the product results are subsequently inputting into a factorization machine model that capable of feature crossover to get the predicted friendship. In addition, we use the federated learning framework to train the GCAFM model, which ensures the privacy of social network data. The experimental results on Douban and FilmTrust social network datasets show that the GCAFM model exceeds four comparison models under various kinds of evaluation metrics. The GCAFM model scored 0.856 and 0.850 under the AUC metric, 0.683 and 0.702 under the AUPR metric, 0.805 and 0.844 under the Precision metric on Douban and FilmTrust datasets.