Top Tweets for #graphmachinelearning
The preprint of our KDD25 paper "Finding Counterfactual Evidences for Node Classification" is now available at: https://t.co/Prh1jGIhFa @DazhuoQiu @FrancescoBonchi @kdd_news #KDD25 #KDD2025 #ExplainableAI #GraphMachineLearning #Counterfactual
Thrilled to get our research paper accepted at #KDD25 #KDD2025 @acmsigkdd "Finding Counterfactual Evidences for Node Classification" - Dazhuo Qiu @DazhuoQiu , Jinwen Chen, Arijit Khan, Yan Zhao, Francesco Bonchi @FrancescoBonchi. Stay tuned for the camera ready!
🫀❤️Thrilled to share our Valentine's Day preprint on AI mapping the human heart! Using #GraphMachineLearning, we're bridging the gap between contact & non-contact catheters for AF mapping. 1/5
A preprint of our workshop report ''LLM+KG: Data Management Opportunities in Unifying Large Language Models+Knowledge Graphs'' is available here: https://t.co/NPokY3GlPy
#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: https://t.co/oYaee8EbbZ
Blog post (https://t.co/7CgOHztzKr) 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 https://t.co/WcojyylRMH of our paper ''View-based Explanations for Graph Neural Networks'' accepted at @SIGMODConf
#SIGMOD2024 #SIGMOD24 #GraphMachineLearning #GraphDataManagement #DatabaseViews #ExplainableA
@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).
https://t.co/C0V8E56vYc
Temporal Graph Learning in 2024
https://t.co/6tQlwZQbdV
✍️@geneweng via @TDataScience
h/t @linked_do
#AI #MachineLearning #DeepLearning #TemporalGraph #GraphMachineLearning #Thoughts #Theory
@mvollmer1 @sonu_monika @JagersbergKnut @EstelaMandela @enilev @BetaMoroney @Shi4Tech @sim010101 @sallyeaves @PerBBerggreen @ahier @NeiraOsci @tlloydjones @TarakRindani @Nicochan33 @YvesMulkers @gerald_bader @HolgerGelhausen @jenstirrup @ChuckDBrooks @SpirosMargaris @LouisColumbus @FractaloidConvo @enricomolinari @Corix_JC @KanezaDiane @AndrewinContact @rvp @FrRonconi @mikeflache @FinMKTG @maponi @Khulood_Almani @sminaev2015 @FernandaKellner @jeancayeux @chidambara09 @theomitsa @timo_vi @sulefati7 @treasadovander @drsharwood @Eli_Krumova @pchamard @SusanHayes_ @RLDI_Lamy @VireousShop
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: https://t.co/xYiEv4Ro1J. #NECLabs
We'll present a poster on "Explainability Methods for GNNs: Towards Usability, Robustness, and Benchmarking" @ D3A – Danish Digitalization, Data Science and AI conference 2024. #GraphMachineLearning, #GraphDataManagement #ExplainableAI https://t.co/PWw3FbTTSQ @DataScienceDK
Our demo on benchmarking of interpretability methods for graph neural networks accepted in GRADES-NDA23 @gradesnda (co-located w/ SIGMOD23 @SIGMODConf)
Interpretability Methods for Graph Neural Networks (Demo) - E. B. Mobaraki & A. Khan
#xAI #ExplainableAI #GraphMachineLearning
We have an open research internship position on the design of deep GNNs! Please help spread the word!
#GNN #GraphMachineLearning #MachineLearning #GraphMining
An internship position is open to work on Deep Graph Neural Networks at @centralesupelec, @Inria_Saclay, and @telecomparis. The intern will be jointly supervised between @frank8m and myself. Please spread the voice 😄. For further details please see: https://t.co/U8sbztOU8x
Deep Graph Library v0.9 has been released and comes with https://t.co/kqN2nwbE5z cuGraph integration. https://t.co/kqN2nwbE5z https://t.co/yWaOCHsIHT #GraphMachineLearning
A specific conference for learning on graphs! Please submit your important work here! Let's work together to build a great community on Graph Machine Learning! #graphmachinelearning
Here it is: the first Learning on Graphs Conference! 🎊
We think this new venue will be valuable for the Graph/Geometric Machine Learning community.
What makes it so important+unique? See our blog post!
https://t.co/WPPbxxLjrE
1/6
TigerGraph Machine Learning Workbench is in preview and comes with a custom Jupyter Lab, plus lots of goodies to hook up your favorite graph ML framework. Exciting times ahead. @TigerGraphDB https://t.co/68Xe35GPOt #GraphMachineLearning
My article on drug repurposing is now also published on the @TigerGraphDB blog. Thanks to the great Tigers and @CayleyWetzig in particular. https://t.co/uMV1aTvUrr
#healthcare #GraphMachineLearning #KnowledgeGraph
Our submission to @KGConference was accepted! Can’t wait to see y’all there #Python #MachineLearning #GraphMachineLearning
Managing Data Sources in Machine Learning: https://t.co/BWoFWLSj1E #GraphMachineLearning #MachineLearning #DataScience #DataVisualization #AI
Managing Data Sources in Machine Learning: https://t.co/BWoFWLSj1E #GraphMachineLearning #MachineLearning #DataScience #DataVisualization #AI
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 https://t.co/WufbLfBUwT!
Cool work #graphmachinelearning relevant also for #trustworthyAI -> utilizing #graph #machinelearning within drug #discovery and development https://t.co/g8AdANt5S2
Managing Data Sources in Machine Learning: https://t.co/BWoFWLSj1E #GraphMachineLearning #MachineLearning #DataScience #DataVisualization #AI
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