# Network Classification and Categorization

arXiv: https://arxiv.org/abs/1709.04481 · HTML: https://mlsanity.com/paper/1709.04481/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 1 of 1)

- [Leveraging advances in machine learning for the robust classification and interpretation of networks](https://mlsanity.com/paper/2403.13215/) (2024)

## Abstract (arXiv)

To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e.g., social networks vs. web graphs) are distinguishable from one another (using both categories of real-world networks and synthetic graphs). A classification accuracy of 94.2% was achieved using a random forest classifier with both real and synthetic networks. This work makes two important findings. First, real-world networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of an arbitrary network. Second, classifying synthetic networks is trivial as our models can easily distinguish between synthetic graphs and the real-world networks they are supposed to model.