Centrality Measures in multi-layer Knowledge Graphs

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...

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Bibliographic Details
Published in:arXiv.org 2022-03
Main Authors: Dörpinghaus, Jens, Weil, Vera, Düing, Carsten, Sommer, Martin W
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Data integration
Graphs
Knowledge representation
Multilayers
Network analysis
Social networks
Topology
Online Access:Get full text
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Summary: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.
ISSN:2331-8422