#graphmachinelearning نتائج البحث

@KuzuDB has straightforward integrations with Polars, NetworkX, Pandas, PyArrow and...Torch Geometric! Meaning that you can export data towards #GraphMachineLearning with one method call. If only they had a bolt driver for JavaScript (hint). kuzudb.com

TheOrbifold's tweet image. @KuzuDB has straightforward  integrations with Polars, NetworkX, Pandas, PyArrow and...Torch Geometric! Meaning that you can export data towards #GraphMachineLearning with one method call. If only they had a bolt driver for JavaScript (hint).
kuzudb.com

Thanks to #DrPlabanBhowmick for his excellent theory session on the 'Introduction to #GraphMachineLearning' on the eve of the 5-Day Mid Career Training Program (#MCTP) on the Foundation Course on #AI and #BigData for #ISS officers at #IITKharagpur on November 13. @plabanb

sabyasachi_uni's tweet image. Thanks to #DrPlabanBhowmick for his excellent theory session on the 'Introduction to #GraphMachineLearning' on the eve of the 5-Day Mid Career Training Program (#MCTP) on the Foundation Course on #AI and #BigData for #ISS officers at #IITKharagpur on November 13. @plabanb

You can now use PyTorch Geometric (PyG) on Graphcore IPUs to accelerate #GraphMachineLearning. buff.ly/3zSxVCc @PyG_Team

TheOrbifold's tweet image. You can now use PyTorch Geometric (PyG) on Graphcore IPUs to accelerate #GraphMachineLearning. buff.ly/3zSxVCc @PyG_Team

The graph of primes exhibits some striking patterns and anomalies, both topologically and in its centrality measures.Can #GraphMachineLearning help here? Based on research with Soumya Jyoti Banerjee buff.ly/3FbO0WM #graphs

TheOrbifold's tweet image. The graph of primes exhibits some striking patterns and anomalies, both topologically and in its centrality measures.Can #GraphMachineLearning help here? Based on research with Soumya Jyoti Banerjee buff.ly/3FbO0WM #graphs

The Deep Graph Library (DGL) reached v1.0 and is packed with new features, like hypergraph neural nets, CUDA streams, hetero graph explainers and more. buff.ly/3Y4nFRi #GraphMachineLearning

TheOrbifold's tweet image. The Deep Graph Library (DGL) reached v1.0 and is packed with new features, like hypergraph neural nets, CUDA streams, hetero graph explainers and more. buff.ly/3Y4nFRi #GraphMachineLearning

GraphScope (by Alibaba) is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster through a user-friendly Python interface buff.ly/3LG76Yp #GraphMachineLearning #Graphs

TheOrbifold's tweet image. GraphScope (by Alibaba) is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster through a user-friendly Python interface  buff.ly/3LG76Yp #GraphMachineLearning #Graphs

Kùzu as a Pytorch Geometric (PyG) Remote Backend: you can now train PyG GNNs and other models directly using graphs (and node features) stored inside @kuzudb . buff.ly/3GGt8Yp #GraphMachineLearning #GraphDatabase

TheOrbifold's tweet image. Kùzu as a Pytorch Geometric (PyG) Remote Backend: you can now train PyG GNNs and other models directly using graphs (and node features) stored inside @kuzudb .  buff.ly/3GGt8Yp #GraphMachineLearning #GraphDatabase

Using #GraphMachineLearning when your data is not a #graph? K-nearest neighbor graphs are not a good solution in fully supervised #machinelearning scenarios. Learn why in the #NeurIPS23 paper by @federico_errica. Read here: neclab.eu/research-areas…. #NECLabs

NECLabsEU's tweet image. Using #GraphMachineLearning when your data is not a #graph? K-nearest neighbor graphs are not a good solution in fully supervised #machinelearning scenarios. Learn why in the #NeurIPS23 paper by @federico_errica. Read here: neclab.eu/research-areas…. #NECLabs

🔥 Read our Highly Cited Paper 📚Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method 🔗mdpi.com/2076-3417/13/1… 👨‍🔬 by Dr. Ersin Şahin et al. #graphconvolutionalnetwork #graphmachinelearning

Applsci's tweet image. 🔥 Read our Highly Cited Paper  
📚Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
🔗mdpi.com/2076-3417/13/1…
👨‍🔬 by Dr. Ersin Şahin et al.   
#graphconvolutionalnetwork #graphmachinelearning

Peng Fang presenting our work ''Distributed Graph Embedding with Information-Oriented Random Walks'' @ #VLDB2023 #VLDB23. There will also be a poster session in the afternoon #GraphMachineLearning #DistributedGraphSystems Paper vldb.org/pvldb/vol16/p1… Blog big-graph-live.blogspot.com/2023/07/distri…

rijitK's tweet image. Peng Fang presenting our work ''Distributed Graph Embedding with Information-Oriented Random Walks'' @ #VLDB2023 #VLDB23. There will also be a poster session in the afternoon #GraphMachineLearning #DistributedGraphSystems
Paper vldb.org/pvldb/vol16/p1…
Blog big-graph-live.blogspot.com/2023/07/distri…

The latest @Neo4j Graph Data Science v2.3 library has (finally) heterogeneous embeddings and an improved Python GDS library. #GraphMachineLearning buff.ly/3YAi1H5 buff.ly/3HOlHz8

TheOrbifold's tweet image. The latest @Neo4j Graph Data Science v2.3 library has (finally) heterogeneous embeddings and an improved Python GDS library. #GraphMachineLearning

buff.ly/3YAi1H5 buff.ly/3HOlHz8

Our paper ''Generating Robust Counterfactual Witnesses for Graph Neural Networks'' by Dazhuo Qiu, Mengying Wang, Arijit Khan, and Yinghui Wu has been accepted at the 40th IEEE International Conference on Data Engineering (ICDE). #ICDE2024 #GraphMachineLearning #ExplainableAI.


Gemini Flash 2.0 really digs deep! Good thing it caught the flaws before reviewers did. Time to tighten those proofs! #GraphMachineLearning


Gemini Flash 2.0 really digs deep! Good thing it caught the flaws before reviewers did. Time to tighten those proofs! #GraphMachineLearning


Thanks to #DrPlabanBhowmick for his excellent theory session on the 'Introduction to #GraphMachineLearning' on the eve of the 5-Day Mid Career Training Program (#MCTP) on the Foundation Course on #AI and #BigData for #ISS officers at #IITKharagpur on November 13. @plabanb

sabyasachi_uni's tweet image. Thanks to #DrPlabanBhowmick for his excellent theory session on the 'Introduction to #GraphMachineLearning' on the eve of the 5-Day Mid Career Training Program (#MCTP) on the Foundation Course on #AI and #BigData for #ISS officers at #IITKharagpur on November 13. @plabanb

🔥 Read our Highly Cited Paper 📚Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method 🔗mdpi.com/2076-3417/13/1… 👨‍🔬 by Dr. Ersin Şahin et al. #graphconvolutionalnetwork #graphmachinelearning

Applsci's tweet image. 🔥 Read our Highly Cited Paper  
📚Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
🔗mdpi.com/2076-3417/13/1…
👨‍🔬 by Dr. Ersin Şahin et al.   
#graphconvolutionalnetwork #graphmachinelearning

A preprint of our workshop report ''LLM+KG: Data Management Opportunities in Unifying Large Language Models+Knowledge Graphs'' is available here: arxiv.org/pdf/2410.01978 #LLMs #KnowledgeGraphs #GraphMachineLearning #GraphDataManagement #GraphRAG #VLDB24 #VLDB2024

🌟Excited for the "LLM+KG: Data Management Opportunities in Unifying Large Language Models + Knowledge Graphs" workshop at #VLDB2024! 🚀 #AI #NLP #KnowledgeGraphs #DataScience #LLM This session will begin at 9:00 AM on August 26th! For more details: seucoin.github.io/workshop/llmkg/

VLDB2024's tweet image. 🌟Excited for the "LLM+KG: Data Management Opportunities in Unifying Large Language Models + Knowledge Graphs" workshop at #VLDB2024!  🚀 #AI #NLP #KnowledgeGraphs #DataScience #LLM

This session will begin at 9:00 AM on August 26th!

For more details: seucoin.github.io/workshop/llmkg/


📢 New article alert! Explore the pioneering framework TEA-GLM, leveraging large language models for zero-shot graph machine learning. Uncover how it enhances effectiveness in zero-shot scenarios. Read more here: bit.ly/4dCc04z #graphmachinelearning #LLMs #TEAGLM


Blog post (big-graph-live.blogspot.com/2024/05/view-b…) for our paper "View-based Explanations for Graph Neural Networks" - T. Chen, D. Qiu, Y. Wu, A. Khan, X. Ke, and Y. Gao, to be presented @SIGMODConf #SIGMOD2024 #SIGMOD24 #GraphMachineLearning #GraphDataManagement #DatabaseViews #ExplainableAI

Find the preprint arxiv.org/abs/2401.02086 of our paper ''View-based Explanations for Graph Neural Networks'' accepted at @SIGMODConf #SIGMOD2024 #SIGMOD24 #GraphMachineLearning #GraphDataManagement #DatabaseViews #ExplainableA



Blog post (big-graph-live.blogspot.com/2024/05/genera…) for our #ICDE2024 #ICDE24 IEEE ICDE Conference paper on "Generating Robust Counterfactual Witnesses for Graph Neural Networks" - D. Qiu, M. Wang, A. Khan, and Y. Wu #GraphMachineLearning #GraphDataManagement #ExplainableAI.

stay tuned for the camera-ready of our #ICDE2024 #ICDE24 IEEE ICDE Conference paper on "Generating Robust Counterfactual Witnesses for Graph Neural Networks" 👇 #GraphMachineLearning #GraphDataManagement #ExplainableAI.



لا توجد نتائج لـ "#graphmachinelearning"

Thanks to #DrPlabanBhowmick for his excellent theory session on the 'Introduction to #GraphMachineLearning' on the eve of the 5-Day Mid Career Training Program (#MCTP) on the Foundation Course on #AI and #BigData for #ISS officers at #IITKharagpur on November 13. @plabanb

sabyasachi_uni's tweet image. Thanks to #DrPlabanBhowmick for his excellent theory session on the 'Introduction to #GraphMachineLearning' on the eve of the 5-Day Mid Career Training Program (#MCTP) on the Foundation Course on #AI and #BigData for #ISS officers at #IITKharagpur on November 13. @plabanb

Using #GraphMachineLearning when your data is not a #graph? K-nearest neighbor graphs are not a good solution in fully supervised #machinelearning scenarios. Learn why in the #NeurIPS23 paper by @federico_errica. Read here: neclab.eu/research-areas…. #NECLabs

NECLabsEU's tweet image. Using #GraphMachineLearning when your data is not a #graph? K-nearest neighbor graphs are not a good solution in fully supervised #machinelearning scenarios. Learn why in the #NeurIPS23 paper by @federico_errica. Read here: neclab.eu/research-areas…. #NECLabs

🔥 Read our Highly Cited Paper 📚Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method 🔗mdpi.com/2076-3417/13/1… 👨‍🔬 by Dr. Ersin Şahin et al. #graphconvolutionalnetwork #graphmachinelearning

Applsci's tweet image. 🔥 Read our Highly Cited Paper  
📚Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
🔗mdpi.com/2076-3417/13/1…
👨‍🔬 by Dr. Ersin Şahin et al.   
#graphconvolutionalnetwork #graphmachinelearning

Excited to announce our next talk (and the first of 2022!): Speaker: @PeterWBattaglia Date/Time: Jan 13, 3:00pm CET Title: Modeling physical structure and dynamics using graph-based machine learning #graphmachinelearning Follow us and register at dsiseminar.github.io!

DegasSeminar's tweet image. Excited to announce our next talk (and the first of 2022!):

Speaker: @PeterWBattaglia 

Date/Time: Jan 13, 3:00pm CET
Title: Modeling physical structure and dynamics using graph-based machine learning #graphmachinelearning

Follow us and register at dsiseminar.github.io!

How to design end-to-end visual solutions for graph machine learning while ensuring a user-friendly experience - a Data61 UX Designer on making data accessible, purposeful, and tangible: bit.ly/2VHKNrX #graphmachinelearning #machinelearning #userexperience


Open source #graphmachinelearning library StellarGraph has launched a series of #algorithms for network graph analysis to discover patterns in #data, work with larger data sets and speed up performance while reducing memory usage. Access the library here: bit.ly/2L6n7Xy


Deep Graph Library v0.9 has been released and comes with buff.ly/2RHeUvi cuGraph integration. buff.ly/3OuCqaU #GraphMachineLearning

TheOrbifold's tweet image. Deep Graph Library v0.9 has been released and comes with buff.ly/2RHeUvi cuGraph integration.    buff.ly/3OuCqaU #GraphMachineLearning

Beyond message passing, a physics-inspired paradigm for graph neural networks. buff.ly/391LJjq #GraphMachineLearning

TheOrbifold's tweet image. Beyond message passing, a physics-inspired paradigm for graph neural networks.  buff.ly/391LJjq #GraphMachineLearning

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