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...
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
