# Learning the mechanisms of network growth

arXiv: https://arxiv.org/abs/2404.00793 · HTML: https://mlsanity.com/paper/2404.00793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 60 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology](https://mlsanity.com/paper/1907.05008/) (2019)
- [Scale-free networks are rare](https://mlsanity.com/paper/1801.03400/) (2018)
- [Quantifying Long-Term Scientific Impact](https://mlsanity.com/paper/1306.3293/) (2013)
- [Choosing to Grow a Graph: Modeling Network Formation as Discrete Choice](https://mlsanity.com/paper/1811.05008/) (2018)
- [Scale-free Networks Well Done](https://mlsanity.com/paper/1811.02071/) (2018)
- [Characterizing the structural diversity of complex networks across domains](https://mlsanity.com/paper/1710.11304/) (2017)
- [Comment on "Quantifying Long-term Scientific Impact"](https://mlsanity.com/paper/1405.1574/) (2014)
- [Predicting Graph Categories from Structural Properties](https://mlsanity.com/paper/1805.02682/) (2018)
- [Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification](https://mlsanity.com/paper/1804.06275/) (2018)
- [Empirically Classifying Network Mechanisms](https://mlsanity.com/paper/2012.15863/) (2020)

## Abstract (arXiv)

We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness.