论文标题
使用动态图神经网络对动态网络的基础和建模：调查
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
论文作者
论文摘要
动态网络用于广泛的领域，包括社交网络分析，推荐系统和流行病学。代表复杂网络随着时间的推移而变化，网络模型不仅可以利用结构性，而且还利用时间模式。但是，随着动态网络文献源于各个领域并利用不一致的术语，导航是一项挑战。同时，近年来，图形神经网络（GNN）在诸如链接预测和节点分类等一系列网络科学任务上表现良好的能力引起了很多关注。尽管图神经网络的流行以及动态网络模型的可靠好处，但对动态网络的图形神经网络几乎没有关注。为了解决这项研究跨越各种领域以及调查动态图神经网络的事实所带来的挑战，这项工作分为两个主要部分。首先，为了解决动态网络术语的歧义，我们建立了具有一致，详细术语和符号的动态网络的基础。其次，我们使用拟议的术语对动态图神经网络模型进行了全面调查
Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems, and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology
