Abstract: Graph has been proven to be an emerging tool for spectrum sensing (SS), with detection performance closely related to the graph characteristics. Existing graph-based SS has been mainly ...

Presenting OpenGraph, a foundation graph model distilling zero-shot graph generalizability from LLMs. To achieve this goal, OpenGraph addresses several key technical challenges: We propose a unified ...

According to Miles Deutscher, traders should use the o3 model for crypto chart analysis and provide detailed context such as timeframe, trading pair, and any unique chart characteristics to enhance ...

Quantum walk applications are divided into 4 main categories: quantum computing, quantum simulation, quantum information processing, and graph-theoretic applications. Quantum walks are a powerful ...

Graph Neural Networks (GNNs) are a rapidly advancing field in machine learning, specifically designed to analyze graph-structured data representing entities and their relationships. These networks ...

Add functions to calc graph characteristics based on the concept of distance between vertices, such as diameter, vertex eccentricity, radius, girth, etc. These metrics are often used in the ...

Graph neural networks (GNNs) have emerged as powerful tools for capturing complex interactions in real-world entities and finding applications across various business domains. These networks excel at ...