Official twitter for Deep Graph Library (DGL) #DGL

Joined December 2018
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Coming to #KDD2024? Mark your day by attending our tutorial "Graph Machine Learning Meets Multi-table Relational Data" on the first afternoon of the conference! @kdd_news @AmazonScience Tutorial details: github.com/dglai/GML-on-Mult…
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DeepGraphLibrary retweeted
16 Feb 2025
🎉 DiskGNN @SIGMODConf : Trained 300M node graphs (TB-scale) with just 1 GPU & <100GB RAM! Zero-overhead GPU/CPU/SSD pooling made it possible 🚀
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Excited to release DBInfer, a comprehensive benchmarking toolbox for evaluating predictive modeling over relational DBs. The DBInfer-Bench package offers 14 benchmarks covering real-world scenarios like recsys, event forecasting, anomaly detection. Repo: github.com/awslabs/multi-tab…
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Key findings from DBInfer-based comparisons of graph neural networks versus strong tabular baselines with feature engineering: * No single winner, a great open space for new models! * GNNs show the strongest performance, but the graph construction strategy matters too. (2/3)
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* Consideration of the end-to-end process from tables to final predictions is critical to success." Paper link: arxiv.org/pdf/2404.18209 A collaborative effort by @AmazonScience DGL team, @sjtu1896 , @PKU1898 , @Tsinghua_Uni , @FudanUni , @CarnegieMellon and many (3/3)

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DGL 2.1 with GPU-accelerated GraphBolt delivers blazing-fast data loading & stays flexible for customization. Plus, it works with PyTorch Geometric (PyG)! 👉Check out the blog for the release summary: dgl.ai/release/2024/03/06/re… 🧠Major Contributor: @mfbalin #DGL #GML

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Say goodbye to data loading bottlenecks! DGL 2.0 introduces GraphBolt, a revolutionary data pipeline framework that supercharges your GNN training. 👉Check out the blog for the release summary: dgl.ai/release/2024/01/26/re…

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Accelerating GNN Training on Intel CPU with DGL through fused sampling & hybrid partitioning, which can give you up to a 2x speedup! 🚀🧠 Awesome work done by Hesham (linkedin.com/in/hesham-mosta…) and Adam (linkedin.com/in/adam-grabows…). @IntelAI @IntelBusiness community.intel.com/t5/Blogs…

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DeepGraphLibrary retweeted
Amazon has publicly released RefChecker, a combination tool and dataset that detects hallucinations in #LLMs. To characterize factual claims, RefChecker uses knowledge triplets rather than natural language, enabling finer-grained judgments. #GenerativeAI amazon.science/blog/new-tool…
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Knowledge graph still rocks in the LLM era! Here is BSChecker🕵️, a new tool leveraging knowledge triplets to flag fine-grain hallucination of LLMs responses in zero/noise/accurate contexts. Demo and Github links below 👇 (1/2)
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DGL team will be joining @LogConference Shanghai local meet-up to share our recent development. Event link: eventbrite.com/e/learning-on… log2023sh.github.io/ . Find out more LoG local meet-ups nearby in the reply 👇
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Will give a talk tomorrow Sunday 10am at the workshop Graph Learning Benchmarks @GLB_Workshop #KDD2023 w/ @YizhouSun, @jimeng, Z. Da @DGLGraph, A. Wang deep-learning-graphs.bitbuck… Thanks to the organizers! Also glad to meet and chat about research during the conference 😀

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Da Zheng @DGLGraph "Graph machine learning for industry applications with DGL and GraphStorm" #KDD2023
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If new with graph learning, here is a warm-up notebook to learn to use graphs: • Build graph w/ features and compute basic message-passing function w/ @DGLGraph • Convert into graph formats w/ DGL, NetworkX, dense/sparse PyTorch • Visualize graph Code: github.com/xbresson/CS6208_2…
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DeepGraphLibrary retweeted
Ever wanted to code Graph Transformer (GT) from *scratch* using a few lines of code with @PyTorch and @DGLGraph? :) See below my course material Slides: dropbox.com/s/i9coadk7fgzf7t… GitHub: github.com/xbresson/CS6208_2… Paper: arxiv.org/pdf/2012.09699.pdf Coding GT step-by-step 👇
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Interested in DGL v1.0? Wanna try out the new sparse APIs for Graph ML? The team will host an hands-on tutorial @TheWebConf'23 on May 1st 1:30pm. Join us! #WWW23 #TheWebConf Video teaser: youtube.com/watch?v=8Zmxnk4O…

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