Finding Motifs in Knowledge Graphs using Compression

TL;DR

This paper presents a novel method for identifying recurring meaningful patterns, called motifs, in knowledge graphs by extending existing techniques and demonstrating their effectiveness on real-world data.

Contribution

It introduces an approach to induce and detect motifs in knowledge graphs, extending previous methods for simple graphs and validating them on real data.

Findings

Motifs are not found in random graphs.

Artificially inserted motifs can be detected.

Method successfully identifies motifs in real-world knowledge graphs.

Abstract

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.