The random graph of Erdos and Renyi is one of the oldest and best studied models of a network, and possesses the considerable advantage of being exactly solvable for many of its average properties.

This paper presents a novel Graph Multi-Resolution Transformer (GMRT) model designed for traffic anomaly detection in environments where autonomous and human-driven vehicles coexist. Utilizing ...

We present a methodology that allows the rapid creation of application models from bandwidth aware core graphs that are available in the literature for a wide range of applications and we discuss ...

proposed a graph machine learning model, namely TREE, based on the Transformer framework. With this novel Transformer-based architecture, TREE not only identifies the most influential omics data ...

and graph traversal. These operations allow for the synthesis of multiple pieces of information, which can then be fed into RAG models to generate richer, more accurate answers to multi-point ...

Our goal is to build a high-performance Knowledge Graph tailored for Large Language Models (LLMs), prioritizing exceptionally low latency to ensure fast and efficient information delivery through our ...

These limitations may obstruct the learning of vulnerable code patterns. In this paper, we propose MAGNET, a Meta-path based Attentional Graph learning model for code vulNErability deTection. We ...

What do you wonder? By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning ...

Preferential attachment models and random graphs are important concepts in network theory, which studies how networks form and evolve over time. These models help explain why some nodes in a ...