# Complex Network Classification with Convolutional Neural Network

arXiv: https://arxiv.org/abs/1802.00539 · HTML: https://mlsanity.com/paper/1802.00539/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification](https://mlsanity.com/paper/2112.13214/) (2021)
- [FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration](https://mlsanity.com/paper/2305.05222/) (2023)
- [Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data](https://mlsanity.com/paper/2101.07914/) (2021)
- [Deep Axial Hypercomplex Networks](https://mlsanity.com/paper/2301.04626/) (2023)
- [AI Learning Algorithms: Deep Learning, Hybrid Models, and Large-Scale Model Integration](https://mlsanity.com/paper/2410.09186/) (2024)
- [Network classification through random walks](https://mlsanity.com/paper/2505.21706/) (2025)

## Abstract (arXiv)

Classifying large scale networks into several categories and distinguishing them according to their fine structures is of great importance with several applications in real life. However, most studies of complex networks focus on properties of a single network but seldom on classification, clustering, and comparison between different networks, in which the network is treated as a whole. Due to the non-Euclidean properties of the data, conventional methods can hardly be applied on networks directly. In this paper, we propose a novel framework of complex network classifier (CNC) by integrating network embedding and convolutional neural network to tackle the problem of network classification. By training the classifiers on synthetic complex network data and real international trade network data, we show CNC can not only classify networks in a high accuracy and robustness, it can also extract the features of the networks automatically.