A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Aldo Pareja, Giacomo Domeniconi, Jie Chen, et al.
EvolveGCN adapts GCN parameters over time using an RNN to handle dynamic graphs without relying on node embeddings.
Saman Marandi, Yu-Shu Hu, Mohammad Modarres
This paper presents a framework for automated construction of Dynamic Master Logic models as knowledge graphs from technical documentation using retrieval-augmented large language models, validated on a nuclear reactor system.
Ling Yue, K. Bhandari, Ching-Yun Ko, et al.
This survey reviews methods for designing and optimizing LLM agent workflows, treating them as agentic computation graphs and organizing literature by when workflow structure is determined.
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This paper introduces a mixture model CNN framework for geometric deep learning on graphs and manifolds, achieving state-of-the-art results in deformable 3D shape analysis.
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This paper presents a unified geometric deep learning framework that generalizes CNNs and other architectures to non-Euclidean domains such as graphs and manifolds.
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This paper provides an overview of geometric deep learning, an emerging field that extends deep learning techniques to non-Euclidean data such as graphs and manifolds.
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This survey systematically reviews Knowledge Distillation on Graphs (KDG), a technique combining graph neural networks and knowledge distillation to deploy efficient models on graphs.
R. Rashmi, V. Upadhya
MAHA is a novel multimodal RAG architecture that uses modality-aware knowledge graphs and hybrid retrieval to improve unstructured data retrieval.
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This paper introduces a Graph RAG approach that integrates Knowledge Graphs and LLMs to contextualize and enrich user requests for dataset discovery, enhancing explainability.
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This paper shows that neural scaling laws emerge even in a simplified setting of random graphs, offering a new theoretical origin for these laws in natural language.
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This paper proposes a Graph RAG-based fault diagnosis system for train bogies that integrates knowledge graphs with large language models to enable dynamic knowledge extraction and improved diagnostic accuracy.
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This paper proposes a graph-based RAG framework that enhances document retrieval by integrating knowledge graphs with LLMs to improve answer accuracy and context relevance.