#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

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

RT Graph ML in 2023: The State of Affairs dlvr.it/SgGRKV #artificialintelligence #deeplearning #graphmachinelearning

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

RT Graph Machine Learning: An Overview dlvr.it/Sm01ws #graph #graphmachinelearning #machinelearning #datascience

RT Graph Machine Learning @ ICML 2023 #graphmachinelearning #artificialintelligence #ai #machinelearning dlvr.it/StGtSf

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

RT Temporal Graph Learning in 2023 dlvr.it/Sgz8fl #graphmachinelearning #thoughtsandtheory #stateoftheartdigest

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

🔥 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…

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
How to Improve Graph Machine Learning Performance! tinyurl.com/5n6w5uc7 #GraphMachineLearning #MachineLearning #Graph #OptimizingGraphMachineLearningModels #StrategiesToBoostGMLPerformance #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine

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

Understanding #GraphMachineLearning in the Era of Large Language Models (#LLMs) isamu-website.medium.com/understanding-…
isamu-website.medium.com
Understanding Graph Machine Learning in the Era of Large Language Models (LLMs)
Hi! These are my notes for a presentation I’m planning for the Huggingface Reading Group on Saturday. If you want to see a recording, check…
🔥 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/

📢 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
🕵️♀️ Explore #GraphMachineLearning for fraud detection with Node Classification! Dive into the world of #GraphML and see how it can help detect fraudulent activities. @Yelp #MachineLearning #memgraph #memgraphdb #graphdatabase #database memgraph.com/blog/4-reasons…
memgraph.com
4 Reasons Why Graph Tech Is Great for Knowledge Graphs
Use graph databases to unify dispersed data, enhance real-time analysis, and scale efficiently with dynamic algorithms.
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.
How to Improve Graph Machine Learning Performance! tinyurl.com/5n6w5uc7 #GraphMachineLearning #MachineLearning #Graph #OptimizingGraphMachineLearningModels #StrategiesToBoostGMLPerformance #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine

🕵️♀️ Explore #GraphMachineLearning for fraud detection with Node Classification! Dive into the world of #GraphML and see how it can help detect fraudulent activities. @Yelp #MachineLearning #memgraph #memgraphdb #graphdatabase #database memgraph.com/blog/4-reasons…
memgraph.com
4 Reasons Why Graph Tech Is Great for Knowledge Graphs
Use graph databases to unify dispersed data, enhance real-time analysis, and scale efficiently with dynamic algorithms.
🕵️♀️ Explore #GraphMachineLearning for fraud detection with Node Classification! Dive into the world of #GraphML and see how it can help detect fraudulent activities. @Yelp #MachineLearning #memgraph #memgraphdb #graphdatabase #database memgraph.com/blog/4-reasons…
memgraph.com
4 Reasons Why Graph Tech Is Great for Knowledge Graphs
Use graph databases to unify dispersed data, enhance real-time analysis, and scale efficiently with dynamic algorithms.
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

RT Graph ML in 2022: Where Are We Now? dlvr.it/SGFGJV #graphmachinelearning #knowledgegraph #deeplearning

RT Temporal Graph Learning in 2023 dlvr.it/Sgz8fl #graphmachinelearning #thoughtsandtheory #stateoftheartdigest

RT Machine learning on graphs, Part 2. dlvr.it/S84PJm #embedding #randomwalk #graphmachinelearning #machinelearning

RT Machine Learning on Graphs, Part 3 dlvr.it/S8lLdv #graphmachinelearning #scalablemachinelearning #graphkernels

RT Graph Machine Learning @ ICML 2023 #graphmachinelearning #artificialintelligence #ai #machinelearning dlvr.it/StGtSf

RT Skip-Gram Neural Network for Graphs dlvr.it/S4H9qZ #graphmachinelearning #nlp #machinelearning #skipgram #ai

RT GraphGPS: Navigating Graph Transformers dlvr.it/SSBHTy #graphmachinelearning #artificialintelligence #computerscience

RT Graph ML in 2023: The State of Affairs dlvr.it/SgGRKV #artificialintelligence #deeplearning #graphmachinelearning

RT Graph Machine Learning: An Overview dlvr.it/Sm01ws #graph #graphmachinelearning #machinelearning #datascience

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

How to Improve Graph Machine Learning Performance! tinyurl.com/5n6w5uc7 #GraphMachineLearning #MachineLearning #Graph #OptimizingGraphMachineLearningModels #StrategiesToBoostGMLPerformance #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine

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

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

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