# Finding Motifs in Knowledge Graphs using Compression

arXiv: https://arxiv.org/abs/2104.08163 · HTML: https://mlsanity.com/paper/2104.08163/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [A tutorial on MDL hypothesis testing for graph analysis](https://mlsanity.com/paper/1810.13163/) (2018)

## Abstract (arXiv)

We introduce a method to find network motifs in knowledge graphs. Network motifs are useful patterns or meaningful subunits of the graph that recur frequently. We extend the common definition of a network motif to coincide with a basic graph pattern. We introduce an approach, inspired by recent work for simple graphs, to induce these from a given knowledge graph, and show that the motifs found reflect the basic structure of the graph. Specifically, we show that in random graphs, no motifs are found, and that when we insert a motif artificially, it can be detected. Finally, we show the results of motif induction on three real-world knowledge graphs.