基于异构动态图模型的社交网络节点分类方法

在机器学习领域,与传统的神经网络相比,图神经网络在社交推荐等任务中发挥着越来越重要的作用,但是目前工作中大多数都使用静态图.针对现有静态图神经网络方法难以考虑社交用户动态特性的问题,通过引入动态图模型提出了一种基于异构动态图模型的社交网络节点分类方法.该方法在动态图建模的基础上,通过基于点边交互的节点特征更新机制和基于循环神经网络的时序聚合方法,实现了高效的动态社交网络节点分类.在多个真实数据集上的实验结果表明,提出方法在动态社交网络数据的节点分类方面有较好的效果,对比静态图和动态图的基准方法有显著的提升.

Social network node classification method based on heterogeneous dynamic graph

In the field of machine learning, graph neural network plays an increasingly important role in works such as social recommendation compared with traditional neural network, but most of the current work use static graph. Aiming at the problem that the existing static graph neural network method is difficult to consider the dynamic characteristics of social users, this paper proposed a method of social network node classification based on a heterogeneous dynamic graph model by introducing a dynamic graph model. On the basis of dynamic graph modeling, this method realized efficient node classification of dynamic social network through node feature updating mechanism based on point-edge interaction and time series aggregation method based on recurrent neural network. The experimental results on multiple real datasets show that the proposed method has a good effect in dynamic social network data node classification, which has a significant improvement compared with the reference method of the static graph and dynamic graph.