# A Generalization of Convolutional Neural Networks to Graph-Structured Data

@article{Hechtlinger2017AGO, title={A Generalization of Convolutional Neural Networks to Graph-Structured Data}, author={Yotam Hechtlinger and Purvasha Chakravarti and Jining Qin}, journal={ArXiv}, year={2017}, volume={abs/1704.08165} }

This paper introduces a generalization of Convolutional Neural Networks (CNNs) from low-dimensional grid data, such as images, to graph-structured data. [... ] Key Method Furthermore, this generalization can be applied to many standard regression or classification problems, by learning the the underlying graph. We empirically demonstrate the performance of the proposed CNN on MNIST, and challenge the state-of-the-art on Merck molecular activity data set. Expand

## 54 Citations

Local-Aggregation Graph Networks

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Learning Representations of Graph Data - A Survey

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- 2019

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- 2021

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CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS

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- 2018

A new spectral domain convolutional architecture for deep learning on graphs with a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest.

Adaptive Filters in Graph Convolutional Neural Networks

- Computer Science
- 2021

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Hypergraph-Induced Convolutional Networks for Visual Classification

- Computer ScienceIEEE Transactions on Neural Networks and Learning Systems
- 2019

The proposed hypergraph-induced convolutional network framework is a framework to explore the high-order correlation in visual data during deep neural networks that combines a hypergraph structure and a learning process based on the constructed hypergraph.

CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters

- Computer ScienceIEEE Transactions on Signal Processing
- 2019

A new spectral domain convolutional architecture for deep learning on graphs with a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest.

Development of Generic CNN Deep Learning Method Using Feature Graph

- Computer Science2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW)
- 2018

This work developed a method to tackle the issue and make CNN applicable by endowing meaning to the sequence of non-structured data, and demonstrated its effectiveness by adding improvements.

Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting

- Computer Science2018 24th International Conference on Pattern Recognition (ICPR)
- 2018

A novel Kernel-Weighted Graph Convolutional Network (KW-GCN) for traffic forecasting, which learns simultaneously a group of convolutional kernels and their linear combination weights for each of the nodes in the graph, which yields a mechanism that is able to learn the features locally and exploit the structure information of traffic road-network globally.

## References

SHOWING 1-10 OF 26 REFERENCES

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

- Computer ScienceNIPS
- 2016

This work presents a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.

Deep Convolutional Networks on Graph-Structured Data

- Computer ScienceArXiv
- 2015

This paper develops an extension of Spectral Networks which incorporates a Graph Estimation procedure, that is test on large-scale classification problems, matching or improving over Dropout Networks with far less parameters to estimate.

Diffusion-Convolutional Neural Networks

- Computer ScienceNIPS
- 2016

Through the introduction of a diffusion-convolution operation, it is shown how diffusion-based representations can be learned from graph-structured data and used as an effective basis for node classification.

Semi-Supervised Classification with Graph Convolutional Networks

- Computer ScienceICLR
- 2017

A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.

Spectral Networks and Locally Connected Networks on Graphs

- Computer ScienceICLR
- 2014

This paper considers possible generalizations of CNNs to signals defined on more general domains without the action of a translation group, and proposes two constructions, one based upon a hierarchical clustering of the domain, and another based on the spectrum of the graph Laplacian.

Geometric Deep Learning: Going beyond Euclidean data

- Computer ScienceIEEE Signal Processing Magazine
- 2017

Deep neural networks are used for solving a broad range of problems from computer vision, natural-language processing, and audio analysis where the invariances of these structures are built into networks used to model them.

The Graph Neural Network Model

- Computer ScienceIEEE Transactions on Neural Networks
- 2009

A new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains, and 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.

Gated Graph Sequence Neural Networks

- Computer ScienceICLR
- 2016

This work studies feature learning techniques for graph-structured inputs and achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.

A new model for learning in graph domains

- Computer ScienceProceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.
- 2005

A new neural model, called graph neural network (GNN), capable of directly processing graphs, which extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs.

Convolutional Networks on Graphs for Learning Molecular Fingerprints

- Computer ScienceNIPS
- 2015

A convolutional neural network that operates directly on graphs that allows end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape is introduced.