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

TL;DR

This paper introduces GMSCN, a scalable and size-independent machine learning approach that accurately identifies the best generative model for a given complex network, outperforming existing methods.

Contribution

The paper presents GMSCN, a novel decision tree-based method for selecting the most suitable generative model for complex networks, emphasizing scalability and size-independence.

Findings

GMSCN achieves higher accuracy than existing methods.

GMSCN is scalable to large networks.

GMSCN is effective across different network sizes.

Abstract

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.