Methods › Graphs › Graph Models › DGCNN

Deep Graph Convolutional Neural Network

DGCNN

49 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

DGCNN involves neural networks that read the graphs directly and learn a classification function. There are two main challenges: 1) how to extract useful features characterizing the rich information encoded in a graph for classification purpose, and 2) how to sequentially read a graph in a meaningful and consistent order. To address the first challenge, we design a localized graph convolution model and show its connection with two graph kernels. To address the second challenge, we design a novel SortPooling layer which sorts graph vertices in a consistent order so that traditional neural networks can be trained on the graphs.

Description and image from: An End-to-End Deep Learning Architecture for Graph Classification

Source: An End-to-End Deep Learning Architecture for Graph Classification

Papers archive 2025-07-28

30 shown of 49, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 81 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Point Cloud Classification8
3D Point Cloud Classification6
Autonomous Driving5
Adversarial Attack4
Graph Neural Network4
Adversarial Robustness3
Classification3
Decision Making3
Knowledge Distillation3
Object3
Self-Supervised Learning3
Semantic Segmentation3
3D Shape Classification2
Data Augmentation2
EEG2
Edge-computing2
GPU2
Neural Architecture Search2
Object Detection2
Object Recognition2

Usage over time archive 2025-07-28

Papers per year tagged with DGCNN: 2018 to 2025, peak 13 13 0 2018: 2 papers 2018 2019: 2 papers 2019 2020: 3 papers 2020 2021: 9 papers 2021 2022: 13 papers 2022 2023: 7 papers 2023 2024: 9 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (49 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Graph Models

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