基于社交网络图节点度的神经网络个性化传播算法研究

Study on Degree of Node Based Personalized Propagation of Neural Predictions forSocial Networks

图是一种重要且基础的数据结构,存在于各种各样的实际场景中。而随着近年来互联网的高速发展,社交网络图数据大量增加,对这些数据进行分析对公共服务、广告营销等实际场景有重要作用。目前已经有不少的图神经网络算法在此类问题中取得了较好的结果,但依然有提升的空间,在很多追求高准确度的场景下,工程师依然希望有性能更好的算法可供选择。文中对神经网个性化传播算法进行了改进,提出了新的可用于社交图网络的图神经网络算法DPPNP。相比于传统图神经网络算法,在信息于节点之间传播时,该算法会根据节点的度对不同节点按不同比例保留自身信息,以提高准确度。在真实数据集上的实验结果表明,与已有的图神经网络算法相比,该算法拥有更好的性能。

Graph is an important and fundamental data structure that presents in a wide variety of practical scenarios.With the rapid development of the Internet in recent years,there has been a huge increase in social network graph data,and the analysis of this data can be of great help in practical scenarios such as public services and advertising and marketing.There are already quite a few graph neural network algorithms that can get good results in such problems,but they still have room for improvement,and in many scenarios where high accuracy is pursued,engineers still want to have algorithms with better performance to choose from.This paper improves personalized propagation of neural predictions and proposes a new graph neural network algorithm called degree of node based personalized propagation of neural predictions(DPPNP)that can be used in social graph networks.Compared to traditional graph neural network algorithms,when the information is propagated between nodes,the proposed algorithm will keep its own information for different nodes in different proportions according to the degree of nodes,so as to improve the accuracy.Experiments on real datasets show that the proposed algorithm has better performance compared to previous graph neural network algorithms.