# Model of complex networks based on citation dynamics

arXiv: https://arxiv.org/abs/1303.5857 · HTML: https://mlsanity.com/paper/1303.5857/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2013.

## Built on by (most cited first, 6 of 6)

- [A Broader Picture of Random-walk Based Graph Embedding](https://mlsanity.com/paper/2110.12344/) (2021)
- [A Robust and Generalized Framework for Adversarial Graph Embedding](https://mlsanity.com/paper/2105.10651/) (2021)
- [DUPLEX: Dual GAT for Complex Embedding of Directed Graphs](https://mlsanity.com/paper/2406.05391/) (2024)
- [Sparse Vicious Attacks on Graph Neural Networks](https://mlsanity.com/paper/2209.09688/) (2022)
- [Leveraging Non-linear Dimension Reduction and Random Walk Co-occurrence for Node Embedding](https://mlsanity.com/paper/2602.24069/) (2026)
- [Graph Data Augmentation via Contrastive Generator Inversion (textttDCBA)](https://mlsanity.com/paper/2610.05653/) (2026)

## Abstract (arXiv)

Complex networks of real-world systems are believed to be controlled by common phenomena, producing structures far from regular or random. These include scale-free degree distributions, small-world structure and assortative mixing by degree, which are also the properties captured by different random graph models proposed in the literature. However, many (non-social) real-world networks are in fact disassortative by degree. Thus, we here propose a simple evolving model that generates networks with most common properties of real-world networks including degree disassortativity. Furthermore, the model has a natural interpretation for citation networks with different practical applications.