论文标题
de bruijn进行神经：有关动态图的时间序列数据的因果感知图神经网络
De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs
论文作者
论文摘要
我们介绍了de bruijn图神经网络（DBGNNS），这是一种新颖的时间感知图神经网络架构，用于动态图上的时间分辨数据。我们的方法解释了在动态图的因果拓扑中展开的时间流行模式，该模式由因果步行确定，即节点可以随着时间的推移可以相互影响的链接序列。我们的体系结构建立在多层de Bruijn图的多层，这是一种迭代的线图结构，其中d de Bruijn阶的节点k表示长度k-1的步行，而边缘则表示长度k的步行。我们开发了一个图形神经网络体系结构，该架构利用de bruijn图来实现遵循非马克维亚动力学的消息传递方案，这使我们能够在动态图的因果拓扑中学习模式。解决de bruijn图形不同订单k的问题可用于建模相同的数据集，我们进一步应用统计模型选择以确定用于消息传递的最佳图形拓扑。合成和经验数据集的评估表明，DBGNN可以利用动态图中的时间模式，从而大大提高了监督节点分类任务中的性能。
We introduce De Bruijn Graph Neural Networks (DBGNNs), a novel time-aware graph neural network architecture for time-resolved data on dynamic graphs. Our approach accounts for temporal-topological patterns that unfold in the causal topology of dynamic graphs, which is determined by causal walks, i.e. temporally ordered sequences of links by which nodes can influence each other over time. Our architecture builds on multiple layers of higher-order De Bruijn graphs, an iterative line graph construction where nodes in a De Bruijn graph of order k represent walks of length k-1, while edges represent walks of length k. We develop a graph neural network architecture that utilizes De Bruijn graphs to implement a message passing scheme that follows a non-Markovian dynamics, which enables us to learn patterns in the causal topology of a dynamic graph. Addressing the issue that De Bruijn graphs with different orders k can be used to model the same data set, we further apply statistical model selection to determine the optimal graph topology to be used for message passing. An evaluation in synthetic and empirical data sets suggests that DBGNNs can leverage temporal patterns in dynamic graphs, which substantially improves the performance in a supervised node classification task.
