Finding Motifs in Knowledge Graphs using Compression
This work addresses motif discovery for knowledge graph analysis, but it is incremental as it adapts existing methods from simple graphs.
The authors tackled the problem of identifying network motifs in knowledge graphs by extending definitions and using a compression-based method, showing that motifs reflect graph structure and can be detected in synthetic and real-world graphs.
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.