Centrality Measures in multi-layer Knowledge Graphs

TL;DR

This paper explores how multi-layer knowledge graphs influence centrality measures like degree and betweenness, revealing that graph topology significantly affects their robustness and suggesting directions for further research.

Contribution

It develops an experimental environment to evaluate centrality measures on multi-layer graphs inspired by social networks, highlighting the impact of graph structure on measure stability.

Findings

Graph topology greatly influences centrality measure robustness.

Multi-layer graphs affect the stability of degree and betweenness centrality.

Experimental environment facilitates analysis of multi-layer graph effects.

Abstract

Knowledge graphs play a central role for linking different data which leads to multiple layers. Thus, they are widely used in big data integration, especially for connecting data from different domains. Few studies have investigated the questions how multiple layers within graphs impact methods and algorithms developed for single-purpose networks, for example social networks. This manuscript investigates the impact of multiple layers on centrality measures compared to single-purpose graph. In particular, (a) we develop an experimental environment to (b) evaluate two different centrality measures - degree and betweenness centrality - on random graphs inspired by social network analysis: small-world and scale-free networks. The presented approach (c) shows that the graph structures and topology has a great impact on its robustness for additional data stored. Although the experimental analysis of random graphs allows us to make some basic observations we will (d) make suggestions for additional research on particular graph structures that have a great impact on the stability of networks.