Identifying modifiable risk factors is crucial for dementia prevention, a global health concern. Depression is considered a risk factor for dementia, but the temporal dynamics across the life course ...

Inspired by self-supervised learning, it uses a cross-view contrastive learning technique, splitting the graph into spatial and temporal views, designing specific graph neural networks, and using ...

Photo Credit: TefiM The following is a summary of “Primary care prediction of hip and knee replacement 1-5 years in advance using temporal graph-based convolutional neural networks (TG-CNNs),” ...

Recent Use of Conditional Spatio-temporal Directed Graph Convolutional Networks(Cond ST-DGCN) [8] to represent human pose estimation has significantly helped in capturing varying non-local ...

However, selecting an appropriate temporal resolution is a challenging task. In this paper, we propose ZigzagNetVis, a methodology that suggests temporal resolutions potentially relevant for analyzing ...

We present a directed electrostatics strategy integrated as a graph neural network (DESIGNN) approach for predicting stable nanocluster structures on their potential energy surfaces (PESs). The ...

Meet Graphiti: a Python library for building temporal Knowledge Graphs. Graphiti is designed specifically to manage evolving relationships over time by capturing and recording changes in facts and ...