# Leveraging advances in machine learning for the robust classification and interpretation of networks

arXiv: https://arxiv.org/abs/2403.13215 · HTML: https://mlsanity.com/paper/2403.13215/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 71 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [SimGNN: A Neural Network Approach to Fast Graph Similarity Computation](https://mlsanity.com/paper/1808.05689/) (2018)
- [Metrics for Graph Comparison: A Practitioner's Guide](https://mlsanity.com/paper/1904.07414/) (2019)
- [Characterizing the structural diversity of complex networks across domains](https://mlsanity.com/paper/1710.11304/) (2017)
- [Futility Analysis in the Cross-Validation of Machine Learning Models](https://mlsanity.com/paper/1405.6974/) (2014)
- [Feature-Based Classification of Networks](https://mlsanity.com/paper/1610.05868/) (2016)
- [Generative Model Selection Using a Scalable and Size-Independent Complex Network Classifier](https://mlsanity.com/paper/1306.2298/) (2013)
- [Network Classification and Categorization](https://mlsanity.com/paper/1709.04481/) (2017)
- [Network Classification Based Structural Analysis of Real Networks and their Model-Generated Counterparts](https://mlsanity.com/paper/1810.08498/) (2018)

## Abstract (arXiv)

The ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often simulation approaches involve selecting a suitable network generative model such as Erd"os-R'enyi or small-world. However, few tools are available to quantify if a particular generative model is suitable for capturing a given network structure or organization. We utilize advances in interpretable machine learning to classify simulated networks by our generative models based on various network attributes, using both primary features and their interactions. Our study underscores the significance of specific network features and their interactions in distinguishing generative models, comprehending complex network structures, and the formation of real-world networks.