Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classification and link prediction problems. The applications of such methods are vast and diverse, but most of the current work relies on the assumption of a static graph. This assumption does not hold for many highly dynamic systems, where the underlying connectivity structure is non-stationary and is mostly unobserved. Using a static model in these situations may result in sub-optimal performance. In contrast, modeling changes in graph structure with time can provide information about the system whose applications go beyond classification. Most work of this type does not learn effective connectivity and focuses on cross-correlation between nodes to generate undirected graphs. An undirected graph is unable to capture direction of an interaction which is vital in many fields, including neuroscience. To bridge this gap, we developed dynamic effective connectivity estimation via neural network training (DECENNT), a novel model to learn an interpretable directed and dynamic graph induced by the downstream classification/prediction task. DECENNT outperforms state-of-the-art (SOTA) methods on five different tasks and infers interpretable task-specific dynamic graphs. The dynamic graphs inferred from functional neuroimaging data align well with the existing literature and provide additional information. Additionally, the temporal attention module of DECENNT identifies time-intervals crucial for predictive downstream task from multivariate time series data.
翻译:最近,代表数据作为图表的方法,如图形神经网络(GNNS),已被成功地用于学习数据表达和结构,以解决分类和连接预测问题。这些方法的应用是广泛而多样的,但目前大多数工作都依赖于静态图形的假设。这一假设对于许多高度动态的系统来说并不有效,这些系统的基本连通结构不是静止的,而且大多没有观测到。在这些情况下,使用静态模型可能导致亚优性性性能。相比之下,用图形结构的模型来建模能够提供有关应用超出分类的系统的信息。这种类型的大多数工作并不学习有效的多功能连接,而侧重于节点之间的交叉联系以生成非定向图表。一个非定向的图表无法捕捉许多领域至关重要的互动方向,包括神经科学。为了弥合这一差距,我们通过神经网络培训(DEENNT)开发了动态有效的连接估计动态有效动态,这是学习下游分类/定位任务所引的可解释性和动态图形的新模型,DENTA/DERNB/CR-C-CRentrental-de-deal-deal-deal-ligal-deal-lifal-ligal-tradal-tradal-dal-tradal-dal-de-de-dal-dal-dal-dol-deal-dal-deal-dal-dal-dal-dal-dal-dal-dal-dal-dal-dal-dal-dal-dal-dal-sxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx