An end-to-end deep learning architecture for graph classification

M Zhang, Z Cui, M Neumann, Y Chen - Proceedings of the AAAI …, 2018 - ojs.aaai.org
Neural networks are typically designed to deal with data in tensor forms. In this paper, we
propose a novel neural network architecture accepting graphs of arbitrary structure. Given a …

Net: Degree-specific graph neural networks for node and graph classification

J Wu, J He, J Xu - Proceedings of the 25th ACM SIGKDD international …, 2019 - dl.acm.org
Graph data widely exist in many high-impact applications. Inspired by the success of deep
learning in grid-structured data, graph neural network models have been proposed to learn …

Are powerful graph neural nets necessary? a dissection on graph classification

T Chen, S Bian, Y Sun - arXiv preprint arXiv:1905.04579, 2019 - arxiv.org
Graph Neural Nets (GNNs) have received increasing attentions, partially due to their
superior performance in many node and graph classification tasks. However, there is a lack …

Graph convolutional networks with eigenpooling

Y Ma, S Wang, CC Aggarwal, J Tang - Proceedings of the 25th ACM …, 2019 - dl.acm.org
Graph neural networks, which generalize deep neural network models to graph structured
data, have attracted increasing attention in recent years. They usually learn node …

How powerful are graph neural networks?

K Xu, W Hu, J Leskovec, S Jegelka - arXiv preprint arXiv:1810.00826, 2018 - arxiv.org
Graph Neural Networks (GNNs) are an effective framework for representation learning of
graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector …

Active and semi-supervised graph neural networks for graph classification

Y Xie, S Lv, Y Qian, C Wen… - IEEE Transactions on Big …, 2022 - ieeexplore.ieee.org
Graph classification aims to predict the class labels of graphs and has a wide range of
applications in many real-world domains. However, most of existing graph neural networks …

Deep graph learning: Foundations, advances and applications

Y Rong, T Xu, J Huang, W Huang, H Cheng… - Proceedings of the 26th …, 2020 - dl.acm.org
Many real data come in the form of non-grid objects, ie graphs, from social networks to
molecules. Adaptation of deep learning from grid-alike data (eg images) to graphs has …

Graph convolutional networks with multi-level coarsening for graph classification

Y Xie, C Yao, M Gong, C Chen, AK Qin - Knowledge-Based Systems, 2020 - Elsevier
Graph convolutional networks (GCNs) have attracted increasing attention in recent years.
Many important tasks in graph analysis involve graph classification which aims to map a …

Convolutional kernel networks for graph-structured data

D Chen, L Jacob, J Mairal - International Conference on …, 2020 - proceedings.mlr.press
We introduce a family of multilayer graph kernels and establish new links between graph
convolutional neural networks and kernel methods. Our approach generalizes convolutional …

A fair comparison of graph neural networks for graph classification

F Errica, M Podda, D Bacciu, A Micheli - arXiv preprint arXiv:1912.09893, 2019 - arxiv.org
Experimental reproducibility and replicability are critical topics in machine learning. Authors
have often raised concerns about their lack in scientific publications to improve the quality of …