# Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier

arXiv: https://arxiv.org/abs/1306.2298 · HTML: https://mlsanity.com/paper/1306.2298/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2013.

## 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)

Real networks exhibit nontrivial topological features such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree for model selection. Our proposed method, which is named "Generative Model Selection for Complex Networks" (GMSCN), outperforms existing methods with respect to accuracy, scalability and size-independence.