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In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs) Framework.
PyTorch-BigGraph: A Large-scale Graph Embedding System
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Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation
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How Powerful are Graph Neural Networks? ICLR
Xu Keyulu, Hu Weihua, Leskovec Jure, and Jegelka Stefanie. 2019 · 2019
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Scaling graph neural networks with approximate pagerank. In KDD
Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann. 2020 · 2020
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LANNS: a web-scale approximate nearest neighbor lookup system
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Grale: Designing Networks for Graph Learning. KDD
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Attribute Graph Neural Networks for Strict Cold Start Recommendation
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Nxtpost: User to post recommendations in facebook groups. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 3792–3800
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DeepGNN is a framework for training machine learning models on large scale graph data
Alex Samylkin. 2022 · 2022
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adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads
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Relational Deep Learning: Graph Representation Learning on Relational Databases
Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, and Jure Leskovec. 2023 · 2023
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Cited alongside, same era.
Temporal graph networks for deep learning on dynamic graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020 · 2020
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Graph Neural Networks for Friend Ranking in Large-Scale Social Platforms. KDD
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Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey
Joakim Skarding, Bogdan Gabrys, and Katarzyna Musial. 2021 · 2021
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GraphCast: Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Alexander Pritzel, Suman Ravuri, Timo Ewalds, Ferran Alet, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Jacklynn Stott, Oriol Vinyals, Shakir Mohamed, and Peter Battaglia. 2022 · 2022
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PinnerFormer: Sequence Modeling for User Representation at Pinterest. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 3702–3712
Nikil Pancha, Andrew Zhai, Jure Leskovec, and Charles Rosenberg. 2022 · 2022
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Que2Search: Fast and Accurate Query and Document Understanding for Search at Facebook. KDD
Yiqun Liu, Kaushik Rangadurai, Yunzhong He, Siddarth Malreddy, Xunlong Gui, Xiaoyi Liu, and Fedor Borisyuk. 2021b
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Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching. CIKM
Zongtao Liu, Bin Ma, Quan Liu, Jian Xu, and Bo Zheng. 2021a
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MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, and Neil Shah. 2023 · 2023
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A Multi-Strategy-Based Pre-Training Method for Cold-Start Recommendation
Bowen Hao, Hongzhi Yin, Jing Zhang, Cuiping Li, and Hong Chen. 2023 · 2023
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Uncertainty-Aware Consistency Learning for Cold-Start Item Recommendation. In SIGIR
Taichi Liu, Chen Gao, Zhenyu Wang, Dong Li, Jianye Hao, Depeng Jin, and Yong Li. 2023 · 2023
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HUGE: Huge Unsupervised Graph Embeddings with TPUs. In KDD
Brandon A. Mayer, Anton Tsitsulin, Hendrik Fichtenberger, Jonathan Halcrow, and Bryan Perozzi. 2023 · 2023
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Fast-track graph ML with GraphStorm: A new way to solve problems on enterprise-scale graphs
Da Zheng and Florian Saupe. 2023 · 2023
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