# A new information dimension of complex networks

arXiv: https://arxiv.org/abs/1311.3527 · HTML: https://mlsanity.com/paper/1311.3527/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2013.

## Built on by (most cited first, 2 of 2)

- [Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis](https://mlsanity.com/paper/2509.08350/) (2025)
- [Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability](https://mlsanity.com/paper/2610.03000/) (2026)

## Abstract (arXiv)

The fractal and self-similarity properties are revealed in many real complex networks. However, the classical information dimension of complex networks is not practical for real complex networks. In this paper, a new information dimension to characterize the dimension of complex networks is proposed. The difference of information for each box in the box-covering algorithm of complex networks is considered by this measure. The proposed method is applied to calculate the fractal dimensions of some real networks. Our results show that the proposed method is efficient for fractal dimension of complex networks.