The Evolution of Intelligent Recommendations with Agentic Graph Systems
The Evolution of Intelligent Recommendations with Agentic Graph Systems ➿ Agentic graph systems for recommendation represent a sophisticated fusion of…
The Evolution of Intelligent Recommendations with Agentic Graph Systems
GraphAgent — An innovative AI agent that efficiently integrates structured and unstructured data
🚀 Excited to Share Our Recent Work! 🌟 GraphAgent — An innovative AI agent that efficiently integrates structured and unstructured data! 📚 👉 Paper link:…
GraphAgent — An innovative AI agent that efficiently integrates structured and unstructured data
Want to catch up on Graph Neural Networks? Now's the time! Graph Neural Networks (GNNs) have become a popular solution for problems that include network data,…
❓How Can Graph Neural Networks Enhance Recommendation Systems by Incorporating Contextual Information? Traditional recommendation systems often leverage a…
Can Graph Learning Improve Planning in LLM-based Agents?
Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests in natural...
ICYMI, here are the slides from our standing room only talk at NeurIPS yesterday! Concepts we discuss include: ➡️ Quantifying how much Transformer you need to… | 18 comments on LinkedIn
Graphs + Transformers = the best of both worlds 🤝 The same models powering breakthroughs in natural language processing are now being adapted for graphs…
GNN: Graph Neural Network and Large Language Model Based for Data Discovery
Our algorithm GNN: Graph Neural Network and Large Language Model Based for Data Discovery inherits the benefits of [Hoang(2024b)] (PLOD: Predictive Learning Opt
A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab
A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab. Of course I mean to say that Hicham Hallak won the… | 19 comments on LinkedIn
A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab
A collection of Graph Embedding methods in Python. 🧠💎 This repository provides hands-on implementations of essential graph embedding algorithms like: ▪️…
Can Ontologies be seen as General Ledger for AI? Could that be a good way to audit AI systems delivering critical business outcomes? In my quest to develop a… | 69 comments on LinkedIn
🕸️Building a LangGraph agent with graph memory The following community examples demonstrates building an agent using LangGraph. Graphiti is used to… | 24 comments on LinkedIn
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
🌟 TGB 2.0 @NeurIPS 2024 🌟 We are very happy to share that our paper TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs… | 11 comments on LinkedIn
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
More Graph, More Agents: Scaling Graph Reasoning with LLMs
More Graph, More Agents: Scaling Graph Reasoning with LLMs Graph reasoning tasks have proven to be a tough nut to crack for Large Language Models (LLMs).…
More Graph, More Agents: Scaling Graph Reasoning with LLMs
Graphs Neural Networks (GNNs) and LLMs are colliding in exciting ways
Graphs Neural Networks (GNNs) and LLMs are colliding in exciting ways. 💥 This survey introduces a novel taxonomy for categorizing existing methods that… | 19 comments on LinkedIn
graphgeeks-lab/awesome-graph-universe: A curated list of resources for graph-related topics, including graph databases, analytics and science
A curated list of resources for graph-related topics, including graph databases, analytics and science - graphgeeks-lab/awesome-graph-universe
Awesome Graph Universe 🌐
Welcome to Awesome Graph Universe, a curated list of resources, tools, libraries, and applications for working with graphs and networks. This repository covers everything from Graph Databases and Knowledge Graphs to Graph Analytics, Graph Computing, and beyond.
Graphs and networks are essential in fields like data science, knowledge representation, machine learning, and computational biology. Our goal is to provide a comprehensive resource that helps researchers, developers, and enthusiasts explore and utilize graph-based technologies.
Feel free to contribute by submitting pull requests! 🚀
UltraQuery: going beyond simple one-hop link prediction to answering more complex queries on any graph in the zero-shot fashion better than trainable SOTA
📣 Foundation models for graph reasoning become even stronger - in our new NeurIPS 2024 work we introduce UltraQuery: going beyond simple one-hop link…
UltraQuery: going beyond simple one-hop link prediction to answering more complex queries on any graph in the zero-shot fashion better than trainable SOTA
In this work, we achieve perfect neural execution of several algorithms by forcing the node and edge representations to be from a fixed finite set. Also, the proposed architectural choice allows us to prove the correctness of the learned algorithms for any test data.
🚀 PyG 2.6 is here! 🎉 We’re excited to announce the release of PyG 2.6.0, packed with incredible updates for graph learning! Here’s a quick rundown of what’s… | 14 comments on LinkedIn
Can Graph Reordering Speed Up Graph Neural Network Training? An...
Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative aggregations of...
The growing ubiquity of relational data structured as graphs has underscored the need for graph learning models with exceptional generalization capabilities. However, current approaches often...
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...
