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.
**!!!! Great Talk with Bradley Rees NVIDIA RAPIDS cuGraph lead at KDD 24 Conference !!** We had an excellent discussion about the cuGraph user experience in…
Learning production functions for supply chains with graph neural networks
The global economy relies on the flow of goods over supply chain networks, with nodes as firms and edges as transactions between firms. While we may observe these external transactions, they are...
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
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
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
Can LLMs understand graphs? The results might surprise you. Graphs are everywhere, from social networks to biological pathways. As AI systems become more…
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
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...
GraphReader: Long-Context Processing in AI ... As AI systems tackle increasingly complex tasks, the ability to effectively process and reason over long…
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse from numerous...
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? 🔎…
DiffKG: Knowledge Graph Diffusion Model for Recommendation
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DiffKG: Knowledge Graph Diffusion Model for Recommendation
Giving a Voice to Your Graph: Representing Structured Data for LLMs
By request, here are the slides today from my keynote at the #CVPR workshop on scene graphs (SG2RL)! papers discussed: 1. Talk Like a Graph (ICLR'24) -… | 19 comments on LinkedIn
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.
graphs provide a way to understand and improve current deep learning architectures
Graphs are all you need! Here is how graphs provide a way to understand and improve current deep learning architectures: It’s well known that graph neural… | 20 comments on LinkedIn
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.…
Static Graph Approximations of Dynamic Contact Networks for Epidemic Forecasting
Excited to share that our recent work "Static Graph Approximations of Dynamic Contact Networks for Epidemic Forecasting" is published at Scientific Reports…
Static Graph Approximations of Dynamic Contact Networks for Epidemic Forecasting
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a graph. Question Answering over KGs (KGQA) is the task of...
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
RAG meets GNNs: Integrating graphs into a modern workflow. Knowledge Graphs (KGs) are a powerful way to represent factual knowledge, but querying them with…
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning