From social networks to knowledge graphs: A plea for interdisciplinary approaches

Abstract

Contextual information is a crucial factor in interdisciplinary research on social networks. At the same time, missing linked data and missing methods for large-scale knowledge networks are a serious limitation. Only few researches have addressed the questions between data science, graph theory and social network analysis. This paper seeks to discuss how this gap can be closed. For this, we use an interdisciplinary approach trying to improve the interdisciplinary exchange and method exchange. We investigate how new methods from computer science, in particular knowledge graphs, can be used within the field of SNA. The contributions of this paper are (1) a knowledge graph model representation of a multi-layer social network, (2) the showcase of examples from different domains and (3) a perspective for more interdisciplinary discussion. We present an innovative solution to discuss coming challenges in SNA with methods from the digital humanities, in particular computer science.

Keywords

• Social Network Analysis
• Knowledge Graphs
• Digital Humanities
• Linked Data
• Knowledge networks
• Graph database