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From graph theory to graph neural networks (GNNs): The opportunities of GNNs in power electronics
Graph theory within power electronics, developed over a 50-year span, is continually
evolving, necessitating ongoing research endeavors. Facing with the never-been-seen …
evolving, necessitating ongoing research endeavors. Facing with the never-been-seen …
Can llms effectively leverage graph structural information: when and why
This paper studies Large Language Models (LLMs) augmented with structured data--
particularly graphs--a crucial data modality that remains underexplored in the LLM literature …
particularly graphs--a crucial data modality that remains underexplored in the LLM literature …
Can llms effectively leverage graph structural information through prompts, and why?
Large language models (LLMs) are gaining increasing attention for their capability to
process graphs with rich text attributes, especially in a zero-shot fashion. Recent studies …
process graphs with rich text attributes, especially in a zero-shot fashion. Recent studies …
A metadata-driven approach to understand graph neural networks
Abstract Graph Neural Networks (GNNs) have achieved remarkable success in various
applications, but their performance can be sensitive to specific data properties of the graph …
applications, but their performance can be sensitive to specific data properties of the graph …