Open AccessPosted Content
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu,Matthias Fey,Marinka Zitnik,Yuxiao Dong,Hongyu Ren,Bowen Liu,Michele Catasta,Jure Leskovec +7 more
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TLDR
The OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs, indicating fruitful opportunities for future research.Abstract:
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains, ranging from social and information networks to biological networks, molecular graphs, source code ASTs, and knowledge graphs. For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics. In addition to building the datasets, we also perform extensive benchmark experiments for each dataset. Our experiments suggest that OGB datasets present significant challenges of scalability to large-scale graphs and out-of-distribution generalization under realistic data splits, indicating fruitful opportunities for future research. Finally, OGB provides an automated end-to-end graph ML pipeline that simplifies and standardizes the process of graph data loading, experimental setup, and model evaluation. OGB will be regularly updated and welcomes inputs from the community. OGB datasets as well as data loaders, evaluation scripts, baseline code, and leaderboards are publicly available at this https URL .read more
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Graph Neural Networks: A Review of Methods and Applications
Jie Zhou,Ganqu Cui,Shengding Hu,Zhengyan Zhang,Cheng Yang,Zhiyuan Liu,Lifeng Wang,Changcheng Li,Maosong Sun +8 more
TL;DR: A detailed review over existing graph neural network models is provided, systematically categorize the applications, and four open problems for future research are proposed.
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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
TL;DR: 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.
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WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh,Shiori Sagawa,Henrik Marklund,Sang Michael Xie,Marvin Zhang,Akshay Balsubramani,Weihua Hu,Michihiro Yasunaga,Richard Lanas Phillips,Irena Gao,Tony Lee,Etienne David,Ian Stavness,Wei Guo,Berton A. Earnshaw,Imran S. Haque,Sara Beery,Jure Leskovec,Anshul Kundaje,Emma Pierson,Sergey Levine,Chelsea Finn,Percy Liang +22 more
TL;DR: WILDS is presented, a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, and is hoped to encourage the development of general-purpose methods that are anchored to real-world distribution shifts and that work well across different applications and problem settings.
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Benchmarking Graph Neural Networks
TL;DR: A reproducible GNN benchmarking framework is introduced, with the facility for researchers to add new models conveniently for arbitrary datasets, and a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs).
Journal Article
Benchmarking Graph Neural Networks
TL;DR: A reproducible GNN benchmarking framework is introduced, with the facility for researchers to add new models conveniently for arbitrary datasets, and a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs).
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
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.