# Opportunities and challenges in partitioning the graph measure space of real-world networks

arXiv: https://arxiv.org/abs/2106.10753 · HTML: https://mlsanity.com/paper/2106.10753/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 72 references that are in the map.
- First posted on arXiv 20 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [Evaluating Overfit and Underfit in Models of Network Community Structure](https://mlsanity.com/paper/1802.10582/) (2018)
- [Axioms for Centrality](https://mlsanity.com/paper/1308.2140/) (2013)
- [Topological analysis of the connectome of digital reconstructions of neural microcircuits](https://mlsanity.com/paper/1601.01580/) (2016)
- [Cliques and Cavities in the Human Connectome](https://mlsanity.com/paper/1608.03520/) (2016)
- [Centrality Measures in Complex Networks: A Survey](https://mlsanity.com/paper/2011.07190/) (2020)
- [The Statistical Physics of Real-World Networks](https://mlsanity.com/paper/1810.05095/) (2018)
- [Characterizing the structural diversity of complex networks across domains](https://mlsanity.com/paper/1710.11304/) (2017)
- [On degree-degree correlations in multilayer networks](https://mlsanity.com/paper/1507.04550/) (2015)
- [Science and Facebook: the same popularity law!](https://mlsanity.com/paper/1701.05347/) (2017)

## Abstract (arXiv)

Based on a large dataset containing thousands of real-world networks ranging from genetic, protein interaction, and metabolic networks to brain, language, ecology, and social networks we search for defining structural measures of the different complex network domains (CND). We calculate 208 measures for all networks and using a comprehensive and scrupulous workflow of statistical and machine learning methods we investigated the limitations and possibilities of identifying the key graph measures of CNDs. Our approach managed to identify well distinguishable groups of network domains and confer their relevant features. These features turn out to be CND specific and not unique even at the level of individual CNDs. The presented methodology may be applied to other similar scenarios involving highly unbalanced and skewed datasets.