#graphAI - Twitter Hashtag

about 19 hours ago

Build graph-powered AI agents with @Neo4j & @GoogleCloud Gemini in Boston on Oct 8! Join our free, hands-on workshop to analyze real-world connected data, plus lunch & networking. Register now: https://t.co/MITapnxnKa #Neo4j #Gemini #GenAI #GraphAI https://t.co/pI96vKl6vS

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Kanwal @kanwalvyas

9 days ago

Built AEGIS Agentic Evidence & Graph Intelligence System for the TigerGraph HHGOA Challenge. AEGIS is an agentic fraud investigation system powered by TigerGraph. Demo: https://t.co/CgIFqdDy4y GitHub: https://t.co/tROtrFMi9G #HHGOA #TigerGraph #AgenticAI #FraudDetection #GraphAI

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The Head And Tale @theheadandtale

23 days ago

AI-native patient safety platform Graph AI has raised $13.3 million in a Series A funding round led by Insight Partners. The funding round also saw participation from existing investor Bessemer Venture Partners. https://t.co/m1fDW7wM1K #GraphAI #InsightPartners #Funding #AI

theheadandtale's tweet photo. AI-native patient safety platform Graph AI has raised $13.3 million in a Series A funding round led by Insight Partners. The funding round also saw participation from existing investor Bessemer Venture Partners.
https://t.co/m1fDW7wM1K

#GraphAI #InsightPartners #Funding #AI https://t.co/9g64Al5dAR

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Business Standard

@bsindia

23 days ago

#GraphAI raised $13.3M in a Series A round led by #InsightPartners with participation from #BessemerVenturePartners The capital will fuel the company's expansion across the #UnitedStates and #Europe @UdishaSrivastav https://t.co/FyHJCnSukk

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NPCI

@NPCI_NPCI

28 days ago

Get ready to hear from the best at #GFF2026! Disha Dubey, Senior AI Engineer, NVIDIA, takes the stage for "Graph AI for Real-Time Fraud Detection: Building Intelligent Financial Networks." 🗓️11th September 📍 Cube #GlobalFintechFest #NPCI #Fintech #NVIDIA #GraphAI

NPCI_NPCI's tweet photo. Get ready to hear from the best at #GFF2026!

Disha Dubey, Senior AI Engineer, NVIDIA, takes the stage for "Graph AI for Real-Time Fraud Detection: Building Intelligent Financial Networks."

🗓️11th September
📍 Cube

#GlobalFintechFest #NPCI #Fintech #NVIDIA #GraphAI https://t.co/t4Jv74G0wa

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Geo-spatial Information Science @GsisOffice

3 months ago

🌍 579 views & counting! Tired of blurry change detection? CPaG fixes graph structure chaos with superpixel vertices + neighbor change probabilities—boosting precision for multimodal remote sensing! 🚀 #MCDBreakthrough #StructuralFeatureMagic #RemoteSensingRevolution #GraphAI #CPaGMethod Link[https://t.co/51Tm8DLmvl]

GsisOffice's tweet photo. 🌍 579 views & counting! Tired of blurry change detection? CPaG fixes graph structure chaos with superpixel vertices + neighbor change probabilities—boosting precision for multimodal remote sensing! 🚀 #MCDBreakthrough #StructuralFeatureMagic #RemoteSensingRevolution #GraphAI #CPaGMethod Link[https://t.co/51Tm8DLmvl]

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Firat Tekiner @TekinerFirat

4 months ago

Excited to launch @neo4j Virtual Graph 🚀 Graph reasoning directly on enterprise data. no ETL, no copies, Cypher pushed into Any Lakehouse. Helping make enterprise data accessible to AI agents. https://t.co/pIsaNl4UNE #GraphAI #EnterpriseAI #AIAgents #Snowflake #Databricks

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Fujitsu Research

@fujitsulabs

7 months ago

How do you find the hidden story in a billion-node graph? 🤯 Our new #GraphAI research unveils two powerful techniques to uncover the phenomena behind massive networks: 1️⃣ Tensor Decomposition to find hidden non-linear structures 2️⃣ Meta Graphical Lasso for fast, interpretable factor analysis From cancer research to financial markets, we're making sense of complexity. Dive into the details on our tech blog! 👇 English: https://t.co/HBZbCaaXy7 Japanese: https://t.co/Jt7UlDEcsg #AI #MachineLearning #BigData #DataScience

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Fujitsu Research

@fujitsulabs

7 months ago

Lifting the veil on #GraphAI! Our latest research from #NeurIPS2025 introduces GnnXemplar for natural language explanations and G-NAMRFF for self-interpretable models. Making AI more transparent and trustworthy. 💡 English: https://t.co/VwsvsmZUp2 Japanese: https://t.co/Nz39yl3e7l #ExplainableAI #XAI #Interpretability #TrustworthyAI #GNN #Fujitsu #Innovation

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Fujitsu Research

@fujitsulabs

8 months ago

Training AI on billion-node graphs is a huge challenge. We developed a new pipeline to break the memory barrier and a Self-Adaptive Graph Mixture of Experts (SAGMM) framework to handle diverse graph structures. 🚀 Read our latest research on scaling #GraphAI! English: https://t.co/5zxvS2jaLp Japanese: https://t.co/8LZPHD3Iow #AI #GNN #MachineLearning #BigData #Fujitsu #Innovation #AAAI2026

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Arijit K @rijitK

8 months ago

paper accepted at #ICLR2026 #ICLR26 "ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks" by Yu Zhang, Sean Bin Yang, Arijit Khan, and Cuneyt Gurcan Akcora @cuneytgurcan https://t.co/IcSV1SsApc #GNN #ExplainableAI #AdverserialAttack #GraphAI #GraphML

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RaptorX.ai @raptorxai

9 months ago

How a Tier-1 Bank Caught a $1.2M Mule Ring in 12 Days The signal wasn’t an anomaly, it was a network. Connected devices, shared identities, and coordinated flows revealed the mule ring in real time. know more at raptorx(dot)ai #FraudDetection #RiskAI #GraphAI #RealTimeAI

raptorxai's tweet photo. How a Tier-1 Bank Caught a $1.2M Mule Ring in 12 Days

The signal wasn’t an anomaly, it was a network.
Connected devices, shared identities, and coordinated flows revealed the mule ring in real time.
know more at raptorx(dot)ai

#FraudDetection #RiskAI #GraphAI #RealTimeAI https://t.co/iwhxwtawdI

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yWorks @yworks

9 months ago

We love seeing yFiles for #Streamlit featured in the #Strwythura project to provide visual oversight for entity-resolved #KnowledgeGraphs. 👉 Try the free yFiles integration for Streamlit here: https://t.co/LYqKQPk6al #GraphAI #RAG #yFiles #DataEngineering #StreamlitApp

Connected Data

@Connected_Data

9 months ago

Strwythura: build a Streamlit app for a question/answer chatbot about a specific topic using advanced techniques for knowledge graph and embeddings Many tutorials about GraphRAG recommend delegating the construction of knowledge graph to large language models (LLM). This seems backwards, since you probably already have structured data and other context about your domain on hand. Why discard useful information, then acquiesce to whatever an LLM hallucinates instead? You’re spending more money and time, but getting less accurate results in return. That approach makes no sense in terms of robust engineering. Instead, this tutorial by @pacoid unbundles the processes needed to reach a working AI application. It explains how to construct an entity resolved knowledge graph from structured data sources and unstructured content sources, implementing an ontology pipeline, plus context engineering for optimizing AI application outcomes within a specific domain. The process is enriched by using entity embeddings and graph algorithms to develop an enhanced GraphRAG approach, which implements a question/answer chatbot about a particular domain. This material provides hands-on experience with advanced techniques as well as working code you can use elsewhere. * Using sophisticated NLP pipelines based on spaCy, GLiNER, textgraphs, and related libraries when extracting information from unstructured content. * Leveraging computable semantics based on standards such as RDF, SKOS, etc., to develop and apply domain-specific semantics which guide the embedding models and language models. * Applying graph analytics to make inference from the knowledge graph, to augment the vector embeddings results before presenting content to an LLM for question/answer summarization. * Using declarative methods for LLM integrations (for example, based on DSPy) instead of spending loads of time on exotic prompt definitions. Then using an observability framework (for example, based on Opik) to collect evaluations and experiments, establishing a feedback loop for optimizing the prompts and weights used in LLM integrations. These techniques provide results which are provably better/faster/cheaper than following an “LLM-everything” approach, plus much more oversight for the intentional arrangement of a knowledge graph. This runs locally without lots of cost, including MLOps instrumentation. The code can be easily extended for other AI app use cases, other topics, and more integrations. The workflow begins with entity resolution, then goes on to illustrate lots of useful graph technologies in practice, combined with NetworkX, spaCy, GLiNER, DSPy, LanceDB, Opik, yWorks, etc. See also - Combining Data from Structured and Unstructured Sources to create High-Quality Knowledge Graphs https://t.co/xICpEpqnsB #GraphRAG #LLMs #Python #NLP #MLOps #DataEngineering #MachineLearning -- Connected Data London 2025 brought together leaders and innovators. Were you there? 🎥 Watch the sessions: https://t.co/w61JGmLcxh 📩 Join the community: https://t.co/RMj1EA5vl1 Join community legends and new voices in #CDL25 for all things #KnowledgeGraph #Graph #analytics #datascience #AI #graphDB #SemTech #Ontology

Connected_Data's tweet photo. Strwythura: build a Streamlit app for a question/answer chatbot about a specific topic using advanced techniques for knowledge graph and embeddings

Many tutorials about GraphRAG recommend delegating the construction of knowledge graph to large language models (LLM). This seems backwards, since you probably already have structured data and other context about your domain on hand.

Why discard useful information, then acquiesce to whatever an LLM hallucinates instead? You’re spending more money and time, but getting less accurate results in return. That approach makes no sense in terms of robust engineering.

Instead, this tutorial by @pacoid unbundles the processes needed to reach a working AI application. It explains how to construct an entity resolved knowledge graph from structured data sources and unstructured content sources, implementing an ontology pipeline, plus context engineering for optimizing AI application outcomes within a specific domain.

The process is enriched by using entity embeddings and graph algorithms to develop an enhanced GraphRAG approach, which implements a question/answer chatbot about a particular domain. This material provides hands-on experience with advanced techniques as well as working code you can use elsewhere.

* Using sophisticated NLP pipelines based on spaCy, GLiNER, textgraphs, and related libraries when extracting information from unstructured content.
* Leveraging computable semantics based on standards such as RDF, SKOS, etc., to develop and apply domain-specific semantics which guide the embedding models and language models.
* Applying graph analytics to make inference from the knowledge graph, to augment the vector embeddings results before presenting content to an LLM for question/answer summarization.
* Using declarative methods for LLM integrations (for example, based on DSPy) instead of spending loads of time on exotic prompt definitions. Then using an observability framework (for example, based on Opik) to collect evaluations and experiments, establishing a feedback loop for optimizing the prompts and weights used in LLM integrations.

These techniques provide results which are provably better/faster/cheaper than following an “LLM-everything” approach, plus much more oversight for the intentional arrangement of a knowledge graph.

This runs locally without lots of cost, including MLOps instrumentation. The code can be easily extended for other AI app use cases, other topics, and more integrations.

The workflow begins with entity resolution, then goes on to illustrate lots of useful graph technologies in practice, combined with NetworkX, spaCy, GLiNER, DSPy, LanceDB, Opik, yWorks, etc.

See also - Combining Data from Structured and Unstructured Sources to create High-Quality Knowledge Graphs

https://t.co/xICpEpqnsB

#GraphRAG #LLMs #Python #NLP #MLOps #DataEngineering #MachineLearning

--

Connected Data London 2025 brought together leaders and innovators. Were you there?

🎥 Watch the sessions: https://t.co/w61JGmLcxh
📩 Join the community: https://t.co/RMj1EA5vl1

Join community legends and new voices in #CDL25 for all things #KnowledgeGraph #Graph #analytics #datascience #AI #graphDB #SemTech #Ontology

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Alucard @ImAlucard2

11 months ago

@graphlinq_proto Humans rest. #GraphAI compounds. 🧠💰 $GLQ 🤞👏🙌🫶💯🔥

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Alucard @ImAlucard2

11 months ago

@maxbidding_btc @graphlinq_proto The future of trading isn’t human… it’s #GraphAI. 🤖💸

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ODSC (Open Data Science Conference) AI

@_odsc

11 months ago

At ODSC AI West 2025, Wes Madrigal, CEO of Kurve, Inc., reveals how to unlock True AutoML Across Your Entire Tabular Data Lake with Graphs. 🔗 Register → https://t.co/1hQQRXzg3u #ODSCAI #GraphAI #AutoML #MLOps #DataEngineering #ODSC

_odsc's tweet photo. At ODSC AI West 2025, Wes Madrigal, CEO of Kurve, Inc., reveals how to unlock True AutoML Across Your Entire Tabular Data Lake with Graphs.

🔗 Register → https://t.co/1hQQRXzg3u

#ODSCAI #GraphAI #AutoML #MLOps #DataEngineering #ODSC https://t.co/12G6lzUqJX

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Alucard @ImAlucard2

11 months ago

@graphlinq_proto @Sentism_ai #GraphAI just got Emotionally Intelligent. 🤖💡 $GLQ 🔥

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Business Standard

@bsindia

12 months ago

California-based Graph AI raised $3 million in seed funding led by Bessemer Venture Partners to scale its AI-driven pharmacovigilance platform for global drug safety and compliance. #GraphAI #PharmaSector #DrugSafety | @PeerzadaAbrar https://t.co/YcbHHzFiMW

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CRN India @crndotin

12 months ago

#GraphAI raises $3M seed to transform #pharmacovigilance with #AI - https://t.co/8VZFZ6B5Dy | #channelnews #technews #technology #futuretech

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Crypto Alby

@Crypto_Alby

about 1 year ago

2/4: GraphAI is like the “translation layer” for crypto: Works across 20+ blockchains 🌐 Adds context to transactions 📊 Integrates real-world assets too 💳 #DeFi #DataLayer #GraphAI

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