# Graph Convolutional Networks: analysis, improvements and results

arXiv: https://arxiv.org/abs/1912.09592 · HTML: https://mlsanity.com/paper/1912.09592/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interaction](https://mlsanity.com/paper/2311.15361/) (2023)
- [EGC2: Enhanced Graph Classification with Easy Graph Compression](https://mlsanity.com/paper/2107.07737/) (2021)
- [A Diffusion-based Data Generator for Training Object Recognition Models in Ultra-Range Distance](https://mlsanity.com/paper/2404.09846/) (2024)

## Abstract (arXiv)

In the current era of neural networks and big data, higher dimensional data is processed for automation of different application areas. Graphs represent a complex data organization in which dependencies between more than one object or activity occur. Due to the high dimensionality, this data creates challenges for machine learning algorithms. Graph convolutional networks were introduced to utilize the convolutional models concepts that shows good results. In this context, we enhanced two of the existing Graph convolutional network models by proposing four enhancements. These changes includes: hyper parameters optimization, convex combination of activation functions, topological information enrichment through clustering coefficients measure, and structural redesign of the network through addition of dense layers. We present extensive results on four state-of-art benchmark datasets. The performance is notable not only in terms of lesser computational cost compared to competitors, but also achieved competitive results for three of the datasets and state-of-the-art for the fourth dataset.