An end-to-end deep learning architecture for graph classification
Feb 2, 2018 · In this paper, we propose a novel neural network architecture accepting graphs of arbitrary structure. Given a dataset containing graphs in the form of (G, y) where G is a graph …
Supervised graph classification with Deep Graph CNN
This notebook demonstrates how to train a graph classification model in a supervised setting using the Deep Graph Convolutional Neural Network (DGCNN) [1] algorithm. In supervised …
In this paper, we propose a novel neural network architecture accepting graphs of arbitrary structure. Given a dataset containing graphs in the form of where is a (G, y) G graph and is its …
A deep graph convolutional neural network architecture for graph …
Mar 10, 2023 · Finally, we design an end-to-end Deep Graph Convolutional Neural Network II (DGCNNII) model for graph classification task, which is up to 32 layers deep. And the …
GraphAny: Fully-inductive Node Classification on Arbitrary Graphs
GraphAny is a fully-inductive model for node classification. A single trained GraphAny model performs node classification tasks on any graph with any feature and label spaces.
[2505.09586] Rhomboid Tiling for Geometric Graph Deep Learning …
3 days ago · View a PDF of the paper titled Rhomboid Tiling for Geometric Graph Deep Learning, by Yipeng Zhang and 2 other authors ... we design RTPool, a hierarchical graph clustering …
Deep graph learning for semi-supervised classification
Oct 1, 2021 · Graph learning (GL) can dynamically capture the distribution structure (graph structure) of data based on graph convolutional networks (GCN), and the learning quality of …
A Comprehensive Introduction to Graph Neural Networks (GNNs)
Jul 21, 2022 · Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how to build a …
Graph Classification via Deep Learning with Virtual Nodes
Aug 14, 2017 · In this paper, we use the recently introduced Column Network for the expanded graph, resulting in a new end-to-end graph classification model dubbed Virtual Column …
A collection of important graph embedding, classification and ...
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations. Relevant graph classification benchmark …