Graph Language Models
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Graph Language Models
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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.
CMU CSD PhD Blog - Measuring and Exploiting Network Usable Information
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
Recent Advances in using Machine Learning with Graphs
Latest findings in multiple research directions for handling graph prediction and optimisation
Must read papers on GNN
This repo covers the basics and latest advancements in Graph Neural Networks. 15k+ GitHub ⭐. https://lnkd.in/e6_7uYt9
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...
GNN-RAG: combining LLMs language abilities with GNNs reasoning in RAG style
Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a…
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
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
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...
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
Large Generative Graph Models
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...
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
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? 🔎…
Introducing our 🌟GFormer🌟: A new paradigm for Recommender Systems
🚀 When #Transformers meet Graph Neural Networks (#GNNs)! Introducing our 🌟GFormer🌟: A new paradigm for Recommender Systems. 📄 [#SIGIR2023] 🔥Graph…
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.
DiffKG: Knowledge Graph Diffusion Model for Recommendation
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DiffKG: Knowledge Graph Diffusion Model for Recommendation
GraphEdit: Large Language Models for Graph Structure Learning
🚀 Enhance your graph analytics with 🔥GraphEdit 🔥 - the innovative Large Language Model (#LLM) tailored for graph structure learning and editing! 🌟…
GraphEdit: Large Language Models for Graph Structure Learning
The nature of complex networks book
— Sergey Dorogovtsev (@dorogovtsev)
Foundation Models in Graph & Geometric Deep Learning
In this post, we argue that the era of Graph FMs has already begun and provide a few examples of how one can use them already today.
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
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.…
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...
Are our novel Spatio-Spectral GNNs (S²GNNs) the better alternative to graph transformers?
🤔 Are our novel Spatio-Spectral GNNs (S²GNNs) the better alternative to graph transformers? ➕ S²GNNs (https://lnkd.in/dbrCTuxH, joint work with Simon…
Are our novel Spatio-Spectral GNNs (S²GNNs) the better alternative to graph transformers?
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