IEEE

A Topical Review of Graph Embedding in Graph Neural Networks

Abstract: Graph embeddings map graph-structured data into vector spaces for machine learning tasks. In Graph Neural Networks (GNNs), these embeddings are computed through message passing and support ...
IEEE

Using Graph With Interconnection Intervals Embedding to Discover Potential Threats in the Network

Abstract: Gradually infiltrating through latent malicious connections to form cyberattacks has become a significant threat in cyberspace. Advanced persistent threats (APTs) are such cyberattacks, ...
Journal of Medical Internet Research

Large Language Model–Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases: Development and Usability Study

Building upon this, we compiled a comprehensive sepsis knowledge graph, comprising of 1894 nodes and 2021 distinct relationships. Conclusions: This study represents a pioneering effort in using LLMs, ...
GitHub

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...