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
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! 🚀
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
The Rise of Graph Jobs, The Disappearance of Graph Technology?
The Rise of Graph Jobs, The Disappearance of Graph Technology? Join me for this fun presentation at KGC on May 8, 2024, in NYC, where I delve into a "State of…
Accelerating Graph Analytics and Graph Neural Networks | NVIDIA On-Demand
See how TigerGraph has integrated their massively parallel processing graph database and analytics platform with NVIDIA cuGraph to deliver 100x performance
How to create synthetic datasets to improve graph-based anomaly detection models
How to create synthetic datasets to improve graph-based anomaly detection models? Amazon researchers developed a new method for synthesizing training data for…
How to create synthetic datasets to improve graph-based anomaly detection models
Links
* Python Examples
* JS Examples
* YouTube
Last week we highlighted LangGraph - a new package (available in both Python and JS) to better enable creation of LLM workflows containing cycles, which are a critical component of most agent runtimes. As a part of the launch, we highlighted two simple runtimes:
a second set of use cases for langgraph - multi-agent workflows. In this blog we will cover:What does "multi-agent" mean?Why are "multi-agent" workflows interesting?Three concrete examples of using LangGraph for multi-agent workflowsTwo examples of third-party applications built on top of LangGraph using multi-agent workflows (GPT-Newspaper and CrewAI)Comparison to other frameworks (Autogen and CrewAI)
🦜🕸️LangGraph⚡ Building language agents as graphs ⚡OverviewLangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain.
It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.
It is inspired by Pregel and Apache Beam.
The current interface exposed is one inspired by NetworkX.The main use is for adding cycles to your LLM application.
Crucially, this is NOT a DAG framework.
If you want to build a DAG, you should use just use LangChain Expression Language.Cycles are important for agent-like behaviors, where you call an LLM in a loop, asking it what action to take next.
Hierarchical Navigable Small World (HNSW) is one of the most efficient ways to build indexes for vector databases. The idea is to build a similarity graph and traverse that graph to find the nodes that are the closest to a query vector
We have seen recently a surge in vector databases in this era of generative AI. The idea behind vector databases is to index the data with vectors that relate… | 30 comments on LinkedIn
Graph, machine learning, hype, and beyond: ArangoDB open source multi-model database releases version 3.7
A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.
Nvidia Rapids cuGraph: Making graph analysis ubiquitous
A new open-source library by Nvidia could be the secret ingredient to advancing analytics and making graph databases faster. The key: parallel processing on Nvidia GPUs.
From data to knowledge and AI via graphs: Technology to support a knowledge-based economy
In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies and processes that can support this transition, and what they are about.
Amazon Neptune update: Machine learning, data science, and the future of graph databases
Amazon Neptune just added another query language, openCypher, to its arsenal. That may not sound like a big deal in and of itself, but coupled with updates in machine learning and data science features, it points towards the future of graph databases.
Scalable Graph Learning in the Enterprise: Efficient GNN model training using Kubernetes and smart GPU provisioner
Graph neural networks (GNNs) have emerged as one of the leading solutions for ML applications. Most real-world data can be represented as graphs - see this blog for a comprehensive overview of what use cases are best solved with GNNs and their key advantages.
Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics. — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national
TigerGraph unveils new tool for machine learning modeling
TigerGraph unveiled a new tool that provides users with a dedicated, open source environment for building machine learning models with graph databases.
Nature Machine Intelligence - The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.
DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation? - DataScienceCentral.com
Can Machine Learning Do Symbolic Manipulation? I spent some time over the holidays engaged in a fascinating online conversation. The gist of it was a variation of an argument that has been going on in the realm of artificial intelligence from the time of Minsky and Seymour Papert: Whether it is possible for neural networks to… Read More »DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation?
Colab tutorial on graph neural networks with the JAX+Jraph stack
Excited to share a Colab tutorial on graph neural networks with the JAX+Jraph stack, which we use in-house at DeepMind! Developed by Lisa Wang and Nikola... 12 comments on LinkedIn
