Open AccessPosted Content
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang,Da Zheng,Zihao Ye,Quan Gan,Mufei Li,Xiang Song,Jinjing Zhou,Chao Ma,Lingfan Yu,Yu Gai,Tianjun Xiao,Tong He,George Karypis,Jinyang Li,Zheng Zhang +14 more
TLDR
DGL distills the computational patterns of GNNs into a few generalized sparse tensor operations suitable for extensive parallelization and allows users to easily port and leverage the existing components across multiple deep learning frameworks.Abstract:
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and implementation of Deep Graph Library (DGL). DGL distills the computational patterns of GNNs into a few generalized sparse tensor operations suitable for extensive parallelization. By advocating graph as the central programming abstraction, DGL can perform optimizations transparently. By cautiously adopting a framework-neutral design, DGL allows users to easily port and leverage the existing components across multiple deep learning frameworks. Our evaluation shows that DGL significantly outperforms other popular GNN-oriented frameworks in both speed and memory consumption over a variety of benchmarks and has little overhead for small scale workloads.read more
Citations
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Proceedings ArticleDOI
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
TL;DR: Graph Contrastive Coding (GCC) is designed --- a self-supervised graph neural network pre-training framework --- to capture the universal network topological properties across multiple networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations.
Proceedings ArticleDOI
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
TL;DR: GCC as mentioned in this paper proposes a self-supervised graph neural network pre-training framework to capture the universal network topological properties across multiple networks and leverage contrastive learning to empower graph neural networks.
Journal Article
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
TL;DR: Graph Substructure Networks (GSN) is proposed, a topologically-aware message passing scheme based on substructure encoding that allows for multiple attractive properties of standard GNNs such as locality and linear network complexity, while being able to disambiguate even hard instances of graph isomorphism.
Posted Content
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
TL;DR: A comprehensive taxonomy of representation learning methods for graph-structured data is proposed, aiming to unify several disparate bodies of work and provide a solid foundation for understanding the intuition behind these methods, and enables future research in the area.
Journal ArticleDOI
Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning.
Xiangxiang Zeng,Xiang Song,Tengfei Ma,Xiaoqin Pan,Yadi Zhou,Yuan Hou,Zheng Zhang,Kenli Li,George Karypis,Feixiong Cheng,Feixiong Cheng,Feixiong Cheng +11 more
TL;DR: There have been more than 2.2 million confirmed cases and over 120,000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coro...
References
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Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Posted Content
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
Proceedings ArticleDOI
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Proceedings Article
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke,Sam Gross,Francisco Massa,Adam Lerer,James Bradbury,Gregory Chanan,Trevor Killeen,Zeming Lin,Natalia Gimelshein,Luca Antiga,Alban Desmaison,Andreas Kopf,Edward Z. Yang,Zachary DeVito,Martin Raison,Alykhan Tejani,Sasank Chilamkurthy,Benoit Steiner,Lu Fang,Junjie Bai,Soumith Chintala +20 more
TL;DR: This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.
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DeepWalk: online learning of social representations
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