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Graph Artificial Intelligence in Medicine | Annual Reviews
Graph Artificial Intelligence in Medicine | Annual Reviews
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.
·annualreviews.org·
Graph Artificial Intelligence in Medicine | Annual Reviews
cuGraph and Graph RAG
cuGraph and Graph RAG
**!!!! Great Talk with Bradley Rees NVIDIA RAPIDS cuGraph lead at KDD 24 Conference !!** We had an excellent discussion about the cuGraph user experience in…
cuGraph
·linkedin.com·
cuGraph and Graph RAG
Must read papers on GNN
Must read papers on GNN
This repo covers the basics and latest advancements in Graph Neural Networks. 15k+ GitHub ⭐. https://lnkd.in/e6_7uYt9
·linkedin.com·
Must read papers on GNN
Plan Like a Graph
Plan Like a Graph
An easy trick to improve your LLM results without fine-tuning. Many people know "Few-Shot prompting" or "Chain of Thought prompting". A new (better) method was… | 77 comments on LinkedIn
Plan Like a Graph
·linkedin.com·
Plan Like a Graph
Foundations and Frontiers of Graph Learning Theory
Foundations and Frontiers of Graph Learning Theory
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network architectures...
Foundations and Frontiers of Graph Learning Theory
·arxiv.org·
Foundations and Frontiers of Graph Learning Theory
Multimodal Graph Benchmark
Multimodal Graph Benchmark
Associating unstructured data with structured information is crucial for real-world tasks that require relevance search. However, existing graph learning benchmarks often overlook the rich...
·arxiv.org·
Multimodal Graph Benchmark
GraphReader: Long-Context Processing in AI
GraphReader: Long-Context Processing in AI
GraphReader: Long-Context Processing in AI ... As AI systems tackle increasingly complex tasks, the ability to effectively process and reason over long…
GraphReader: Long-Context Processing in AI
·linkedin.com·
GraphReader: Long-Context Processing in AI
A Survey of Large Language Models for Graphs
A Survey of Large Language Models for Graphs
🚀 What happens when LLMs meet Graphs? 🔍 Excited to share our new [#KDD'2024] Survey+Tutorial on 🌟LLM4Graph🌟: "A Survey of Large Language Models for…
A Survey of Large Language Models for Graphs
·linkedin.com·
A Survey of Large Language Models for Graphs
How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks
How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks
💡 How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks? 🔎…
·linkedin.com·
How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks
This Large Graph Model (LGM) has undergone training on a diverse set of 5,000 graphs across 13 different domains.
This Large Graph Model (LGM) has undergone training on a diverse set of 5,000 graphs across 13 different domains.
This Large Graph Model (LGM) has undergone training on a diverse set of 5,000 graphs across 13 different domains. It serves as a valuable tool for…
This Large Graph Model (LGM) has undergone training on a diverse set of 5,000 graphs across 13 different domains.
·linkedin.com·
This Large Graph Model (LGM) has undergone training on a diverse set of 5,000 graphs across 13 different domains.
DiffKG: Knowledge Graph Diffusion Model for Recommendation
DiffKG: Knowledge Graph Diffusion Model for Recommendation
500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.
DiffKG: Knowledge Graph Diffusion Model for Recommendation
·linkedin.com·
DiffKG: Knowledge Graph Diffusion Model for Recommendation
GraphStorm: all-in-one graph machine learning framework for industry applications
GraphStorm: all-in-one graph machine learning framework for industry applications
Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for scalable graph construction, graph model training and inference. GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code. GraphStorm has been used and deployed for over a dozen billion-scale industry applications after its release in May 2023. It is open-sourced in Github: https://github.com/awslabs/graphstorm.
·arxiv.org·
GraphStorm: all-in-one graph machine learning framework for industry applications
A repo for ICML graph papers
A repo for ICML graph papers
Following ICLR Graph Papers, I've created a repo for ICML graph papers, grouped by topic. We've got around 250 papers focusing on Graphs and GNNs in ICML'24.…
·linkedin.com·
A repo for ICML graph papers