# Clones in Graphs

arXiv: https://arxiv.org/abs/1802.07849 · HTML: https://mlsanity.com/paper/1802.07849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2018.

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

- [Ordinal Motifs in Lattices](https://mlsanity.com/paper/2304.04827/) (2023)
- [Towards Ordinal Data Science](https://mlsanity.com/paper/2307.09477/) (2023)

## Abstract (arXiv)

Finding structural similarities in graph data, like social networks, is a far-ranging task in data mining and knowledge discovery. A (conceptually) simple reduction would be to compute the automorphism group of a graph. However, this approach is ineffective in data mining since real world data does not exhibit enough structural regularity. Here we step in with a novel approach based on mappings that preserve the maximal cliques. For this we exploit the well known correspondence between bipartite graphs and the data structure formal context (G,M,I) from Formal Concept Analysis. From there we utilize the notion of clone items. The investigation of these is still an open problem to which we add new insights with this work. Furthermore, we produce a substantial experimental investigation of real world data. We conclude with demonstrating the generalization of clone items to permutations.