Data-driven graph learning models a network by determining the strength of connections between its nodes. The data refers to a graph signal which associates a value with each graph node. Existing graph learning methods either use simplified models for the graph signal, or they are prohibitively expensive in terms of computational and memory requirements. This is particularly true when the number of nodes is high or there are temporal changes in the network. In order to consider richer models with a reasonable computational tractability, we introduce a graph learning method based on representation learning on graphs. Representation learning generates an embedding for each graph node, taking the information from neighbouring nodes into account. Our graph learning method further modifies the embeddings to compute the graph similarity matrix. In this work, graph learning is used to examine brain networks for brain state identification. We infer time-varying brain graphs from an extensive dataset of intracranial electroencephalographic (iEEG) signals from ten patients. We then apply the graphs as input to a classifier to distinguish seizure vs. non-seizure brain states. Using the binary classification metric of area under the receiver operating characteristic curve (AUC), this approach yields an average of 9.13 percent improvement when compared to two widely used brain network modeling methods.

中文翻译：

---

从数据中以节点为中心的图学习以进行脑状态识别

数据驱动图学习通过确定网络节点之间的连接强度来对网络进行建模。数据是指将值与每个图节点关联的图信号。现有的图形学习方法或者对图形信号使用简化的模型，或者就计算和存储需求而言，它们的成本过高。当节点数量很多或网络中存在时间变化时，尤其如此。为了考虑具有合理的计算可处理性的更丰富的模型，我们介绍了一种基于图表示学习的图学习方法。表示学习将考虑到来自相邻节点的信息，为每个图节点生成一个嵌入。我们的图学习方法进一步修改了嵌入，以计算图相似度矩阵。在这项工作中，图学习用于检查大脑网络以进行大脑状态识别。我们从十名患者的颅内脑电图（iEEG）信号的广泛数据集推断时变的脑图。然后，我们将这些图作为输入应用于分类器，以区分癫痫发作与非癫痫发作的大脑状态。使用接收器工作特性曲线（AUC）下面积的二值分类度量，与两种广泛使用的脑网络建模方法相比，此方法平均可提高9.13％。非癫痫发作的大脑状态。使用接收器工作特性曲线（AUC）下面积的二进制分类度量，与两种广泛使用的脑部网络建模方法相比，此方法平均可提高9.13％。非癫痫发作的大脑状态。使用接收器工作特性曲线（AUC）下面积的二进制分类度量，与两种广泛使用的脑部网络建模方法相比，此方法平均可提高9.13％。