论文标题
evonet：用于预测动态图演变的神经网络
EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs
论文作者
论文摘要
近年来，已经对结构化数据（如图）进行结构化数据的神经网络进行了广泛的研究。迄今为止，大部分研究活动主要集中在静态图上。但是，大多数现实世界网络都是动态的，因为它们的拓扑往往会随着时间而变化。预测动态图的演变是图形挖掘区域中具有很高意义的任务。尽管其实际上很重要，但迄今为止尚未深入探索该任务，这主要是由于其挑战性的性质。在本文中，我们提出了一个预测动态图演化的模型。具体而言，我们使用图形神经网络以及循环结构来捕获动态图的时间演化模式。然后，我们采用一个生成模型，该模型可以在下一个时间步骤预测图形的拓扑，并构建与该拓扑相对应的图实例。我们在通用网络不断发展的动态以及现实世界数据集上评估了几个人工数据集上的建议模型。结果证明了所提出的模型的有效性。
Neural networks for structured data like graphs have been studied extensively in recent years. To date, the bulk of research activity has focused mainly on static graphs. However, most real-world networks are dynamic since their topology tends to change over time. Predicting the evolution of dynamic graphs is a task of high significance in the area of graph mining. Despite its practical importance, the task has not been explored in depth so far, mainly due to its challenging nature. In this paper, we propose a model that predicts the evolution of dynamic graphs. Specifically, we use a graph neural network along with a recurrent architecture to capture the temporal evolution patterns of dynamic graphs. Then, we employ a generative model which predicts the topology of the graph at the next time step and constructs a graph instance that corresponds to that topology. We evaluate the proposed model on several artificial datasets following common network evolving dynamics, as well as on real-world datasets. Results demonstrate the effectiveness of the proposed model.
