# The Graph Neural Network Model

@article{Scarselli2009TheGN, title={The Graph Neural Network Model}, author={Franco Scarselli and Marco Gori and Ah Chung Tsoi and Markus Hagenbuchner and Gabriele Monfardini}, journal={IEEE Transactions on Neural Networks}, year={2009}, volume={20}, pages={61-80} }

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. [] Key Method This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space.

## 3,825 Citations

### Computational Capabilities of Graph Neural Networks

- Computer Science, MathematicsIEEE Transactions on Neural Networks
- 2009

The functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision are described, and includes most of the practically useful functions on graphs.

### An Investigation Into Graph Neural Networks

- Computer Science
- 2020

GCN models can successfully process graph data and also outperforms traditional fully connected networks by 4% of classification accuracy, and it has proven that due to high interpretability, new architectures and libraries, and performance, there is a dramatic increase in applications and research of GNNs as a graphical analysis tool.

### Dual Convolutional Neural Network for Graph of Graphs Link Prediction

- Computer ScienceArXiv
- 2018

A dual convolutional neural network is proposed that extracts node representations by combining the external and internal graph structures in an end-to-end manner.

### Graph Neural Network: A Comprehensive Review on Non-Euclidean Space

- Computer ScienceIEEE Access
- 2021

This review provides a comprehensive overview of the state-of-the-art methods of graph-based networks from a deep learning perspective and formulates a systematic categorization of GNN models according to their applications from theory to real-life problems to encapsulate the importance of graph models.

### Graph neural networks in node classification: survey and evaluation

- Computer ScienceMachine Vision and Applications
- 2021

This paper provides a comprehensive review about applying graph neural networks to the node classification task and discusses the state-of-the-art methods, divided into three main categories: convolutional mechanism, attention mechanism and autoencoder mechanism.

### Dynamic Graph Neural Networks

- Computer ScienceArXiv
- 2018

DGNN is proposed, a new Dynamic Graph Neural Network model, which can model the dynamic information as the graph evolving and can keep updating node information by capturing the sequential information of edges, the time intervals between edges and information propagation coherently.

### Random Walk Graph Neural Networks

- Computer ScienceNeurIPS
- 2020

A more intuitive and transparent architecture for graph-structured data, so-called Random Walk Graph Neural Network (RWNN), which consists of a number of trainable “hidden graphs” which are compared against the input graphs using a random walk kernel to produce graph representations.

### Learning long-term dependencies using layered graph neural networks

- Computer ScienceThe 2010 International Joint Conference on Neural Networks (IJCNN)
- 2010

This paper presents a new architecture, called Layered GNN (LGNN), realized by a cascade of GNNs: each layer is fed with the original data and with the state information calculated by the previous layer in the cascade, which allows each GNN to solve a subproblem.

### GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

- Computer ScienceIEEE Transactions on Knowledge and Data Engineering
- 2022

This paper proposes GraphLIME, a local interpretable model explanation for graphs using the Hilbert-Schmidt Independence Criterion (HSIC) Lasso, which is a nonlinear feature selection method.

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