Top Tweets for #GraphAnalytics
By integrating #GraphDatabases into AI architectures—specifically through #GraphRAG—we can ground #AI outputs in verifiable, connected knowledge.🤖 It's time to move beyond tables and embrace the graph.💡 https://t.co/TVg0LLuxL3
#NebulaGraph #KnowledgeGraph #GraphAnalytics #LLM
Uncover graph-like analytics in PostgreSQL with Nida Fatima at PG DATA 2026 on June 4!
Join us: https://t.co/9bHaHxRifP
#PGData #PGData2026 #PostgreSQL #Postgres #HealthcareData #Analytics #SQL #DataEngineering #OpenSource #GraphAnalytics

NebulaGraph v5.2 is here! 🚀
100x faster path queries + Native Hybrid Search (Graph + Vector + Text) = The ultimate AI-native engine. 🤖
👉 Start computing intelligence: https://t.co/TyAeMtfnJ0
#GraphDatabase #NebulaGraph #AI #LLM #GraphAnalytics #KnowledgeGraph #GraphRAG
As businesses move from simply “storing connections” to “deriving intelligence from connections,” the need for a powerful, flexible, and scalable #GraphAnalytics solution becomes critical. 🤔 This is where NebulaGraph Analytics comes in: https://t.co/gvr2k0w4nJ
#NebulaGraph #AI
Great day at North East AI Agents Day at @JaneStreet NYC. Honored to give a talk on "Agentic Exploration of Large-Scale Graphs with Arkouda and Arachne." Thanks to the organizers for convening such a sharp crowd. #AIAgents #HPC #GraphAnalytics #LLM
https://t.co/NO7oIse0NF

#GraphComputing ≠ graph databases: The former is the analysis layer, the latter is the storage layer. 🤖 Graph computing doesn’t replace your stack—it supercharges it with relationship-aware insights.💡
🔗 https://t.co/NkjokYF4Js
#GraphDatabase #NebulaGraph #GraphAnalytics
#SubgraphComputing is a specialized form of graph computing that focuses on analyzing specific, smaller sections (subgraphs) within a larger graph, rather than processing the entire dataset. 🔍
🔗 Read more:
https://t.co/M0QZw38idn
#GraphDatabase #NebulaGraph #GraphAnalytics
#NebulaGraph Analytics empowers your teams to move beyond pre-packaged solutions and build algorithms that perfectly mirror your unique business logic. 🚀
Come to Try a Platform for the Era of Billion-Relation Analytics:
https://t.co/dSHeZYHMpn
#GraphAnalytics #GraphAlgorithms
By leveraging #NebulaGraph, Envision has constructed a "Whole Grid Graph," transforming disparate devices, data, and business logic into a unified, intelligible network that can be analyzed in real time. 🚀
🔗 https://t.co/OBiZMKOEDu
#GraphDatabase #NebulaGraph #GraphAnalytics
#NebulaGraph Analytics lets you:
✅ Write custom graph algos in #GQL
✅ Generate code from natural language (#AI-assisted)
✅ Analyze data in-place (S3, HDFS, etc.)
✅ Get 5-10x speedup & 80% less resource use
🔗 https://t.co/h9annNovU8
#GraphAnalytics #GraphAlgorithms
This is my #researchproject
Built a Graph-Based KYB System for detecting shell companies & financial fraud.
Used #GraphAnalytics + ML (Isolation Forest, LOF) + Explainable AI
to detect hidden ownership networks.
Key insight: Fraud hides in connections, not individuals.

Neural Knowledge Graph Reasoning with Relational Digraph #GraphTheory #GraphAnalytics #NetworkScience #GraphModels #GraphAlgorithms #GraphStructures #Combinatorics #DiscreteMathematics #RelationalData #RelationalModeling #RelationshipAnalysis #DataRelationships
JUST IN: Learn to build a high-scale graph analytics pipeline using NetworKit 11.2.1. Generate large networks, detect communities, compute cores, and sparsify graphs efficiently for downstream workflows. #GraphAnalytics #NetworKit
🚀 NiamonX Graph is live!
Turn domains, IPs, emails & blockchain addresses into a real-time investigation graph!
40+ transformers
DNS, Threat Intel, Breaches, Blockchain
Built for OSINT, SOC, IR & Law Enforcement
Think in connections. Act faster.
#OSINT #GraphAnalytics #SOC

Algorithms on Trees and Graphs (with Python Code): https://t.co/GHahR5Ryxz
——————
#AI #MachineLearning #DataScience #ML #LinkedData #GraphDB #GraphAnalytics #KnowledgeGraphs #RAG #LLMs #DataScientist

Australian firms are using connected graphs to reveal hidden customer churn risks, predict departures earlier and refine retention strategies, turning complex data into clearer loyalty insights. #CustomerChurn #GraphAnalytics #DataScience #AusBiz https://t.co/Bis36I8aqb
Algorithms on Trees and Graphs (with Python Code): https://t.co/GHahR5Ryxz
——————
#AI #MachineLearning #DataScience #ML #LinkedData #GraphDB #GraphAnalytics #KnowledgeGraphs #RAG #LLMs #DataScientist

Graph Analytics uncovers hidden relationships in complex data using nodes and links, enabling smarter decisions in fraud detection, networks, and recommendation systems.
#GraphAnalytics #BigData #AI #DataScience #NetworkAnalysis
https://t.co/UW5kO3UcJr
#Algorithms on Trees and Graphs (with #Python Code): https://t.co/GHahR5S6n7
——————
#AI #MachineLearning #DataScience #ML #RDF #LinkedData #GraphDB #GraphAnalytics #Semantic #KnowledgeGraphs #RAG #LLMs #DataScientist

Want to speak at Graph the Planet 2026 during RSAC week? We focus on the cross-section of graphs, AI and security. Call for speakers open, check the link below. #RSAC #CallForSpeakers #AgenticAI #GraphAnalytics #AIEngineering #GraphAI #AIAgents #SecurityEngineering #AppliedAI
Announcing the call for speakers for Graph the Planet 2026:
When: March 23rd during RSAC week | One-Day Summit
Where: San Francisco + Livestream
This year’s theme is the Agent Army: Deploying Agentic & Graph Technologies, and welcome all our core themes as usual.
We’re seeking diverse talks spanning:
◼️ Use cases: Security, logs, fraud, crime, misinformation, and data-intensive investigations in general
◼️ Generative AI: Interactive agents, autonomous agents, production, foundations
◼️ Graph technologies: Visualization, analytics, databases, AI
Trustworthy AI: AI Red teaming, GRC, hardening
◼️ Experience reports, new tools & techniques, and leadership perspectives
Join executives, researchers, senior analysts, and founders for a day of talks, panels, demos, and interactive sessions during RSA week. New this year will also be 2 days of optional trainings and improved live streams.
Have experience building and deploying AI or graph systems at scale?
Share your insights by applying in the link in the first comment

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