Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Wang, Minjie; Zheng, Da; Ye, Zihao; Gan, Quan; Li, Mufei; Song, Xiang; Zhou, Jinjing; Ma, Chao; Yu, Lingfan; Gai, Yu; Xiao, Tianjun; He, Tong; Karypis, George; Li, Jinyang; Zhang, Zheng

doi:10.48550/arxiv.1909.01315

Cited by 286 publications

(331 citation statements)

References 17 publications

Supporting

Mentioning

328

Contrasting

Unclassified

Order By: Relevance

“…In order to show its effectiveness, we demonstrate the proposed approach in two multi-view visual perception problems, semantic segmentation and monocular depth estimation. We leverage Deep Graph Library (DGL) [29] Python package and PyTorch [30] to design our approach.…”

Section: F Task-related Lossmentioning

confidence: 99%

Multi-Robot Collaborative Perception With Graph Neural Networks

Zhou

Xiao²,

Zhou³

et al. 2022

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Multi-robot systems such as swarms of aerial robots are naturally suited to offer additional flexibility, resilience, and robustness in several tasks compared to a single robot by enabling cooperation among the agents. To enhance the autonomous robot decision-making process and situational awareness, multi-robot systems have to coordinate their perception capabilities to collect, share, and fuse environment information among the agents efficiently to obtain context-appropriate information or gain resilience to sensor noise or failures. In this paper, we propose a general-purpose Graph Neural Network (GNN) with the main goal to increase, in multi-robot perception tasks, single robots' inference perception accuracy as well as resilience to sensor failures and disturbances. We show that the proposed framework can address multi-view visual perception problems such as monocular depth estimation and semantic segmentation. Several experiments both using photo-realistic and real data gathered from multiple aerial robots' viewpoints show the effectiveness of the proposed approach in challenging inference conditions including images corrupted by heavy noise and camera occlusions or failures.

show abstract

Section: F Task-related Lossmentioning

confidence: 99%

Multi-Robot Collaborative Perception With Graph Neural Networks

Zhou

Xiao²,

Zhou³

et al. 2022

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

“…All methods were implemented using Python 3.8 and Pytorch 1.7.1 library [33]. Graph neural networks were implemented using the DGL graph computing library [34] version 0.6.1. Model parameters were initialized according to [35].…”

Section: A Experimental Detailsmentioning

confidence: 99%

Structure and position-aware graph neural network for airway labeling

Xie¹,

Jacobs²,

Charbonnier³

et al. 2022

Preprint

View full text Add to dashboard Cite

We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks (CNN) and enriched using graph neural networks. Our graph neural network is structure-aware by having each node aggregate information from its local neighbors and position-aware by encoding node positions in the graph.We evaluated the proposed method on 220 airway trees from subjects with various severity stages of Chronic Obstructive Pulmonary Disease (COPD). The results demonstrate that our approach is computationally efficient and significantly improves branch classification performance than the baseline method.The overall average accuracy of our method reaches 91.18% for labeling all 18 segmental airway branches, compared to 83.83% obtained by the standard CNN method. We published our source code at https://github.com/DIAGNijmegen/spgnn. The proposed algorithm is also publicly available at https://grandchallenge.org/algorithms/airway-anatomical-labeling/.

show abstract

“…We implement SUBLIME using PyTorch 1.7.1 [31] and DGL 0.7.1 [44]. All experiments are conducted on a Linux server with an Intel Xeon 4214R CPU and four Quadro RTX 6000 GPUs.…”

Section: F Implementation Details F1 Computing Infrastructuresmentioning

confidence: 99%

Towards Unsupervised Deep Graph Structure Learning

Liu¹,

Zheng²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated when noisy connections occur in the original graph structures; besides, the dependence on explicit structures prevents GNNs from being applied to general unstructured scenarios. To address these issues, recently emerged deep graph structure learning (GSL) methods propose to jointly optimize the graph structure along with GNN under the supervision of a node classification task. Nonetheless, these methods focus on a supervised learning scenario, which leads to several problems, i.e., the reliance on labels, the bias of edge distribution, and the limitation on application tasks. In this paper, we propose a more practical GSL paradigm, unsupervised graph structure learning, where the learned graph topology is optimized by data itself without any external guidance (i.e., labels). To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning. Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph. To provide persistent guidance, we design a novel bootstrapping mechanism that upgrades the anchor graph with learned structures during model learning. We also design a series of graph learners and post-processing schemes to model the structures to learn. Extensive experiments on eight benchmark datasets demonstrate the significant effectiveness of our proposed SUBLIME and high quality of the optimized graphs. CCS CONCEPTS• Mathematics of computing → Graph algorithms; • Computing methodologies → Neural networks.

show abstract

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Cited by 286 publications

References 17 publications

Multi-Robot Collaborative Perception With Graph Neural Networks

Multi-Robot Collaborative Perception With Graph Neural Networks

Structure and position-aware graph neural network for airway labeling

Towards Unsupervised Deep Graph Structure Learning

Contact Info

Product

Resources

About