Centrality measurement on semantically multiplex social networks: divide-and-conquer approach

Semantic technologies exploit to support collaborations in social networks. However, these networks assume that linkages between actors should be ignored, more exactly, semantically identical. For example, in bibliometrics, links on network are described with only 'co-authoring' relationship between the actors. In this paper, we focus on analysing semantically multiplex social networks, representing various relationships between people simultaneously. Especially, we show how to discover important social patterns, from the networks. Thereby, we propose a divide-and-conquer approach based on semantic alignment function, separating the multiplex social networks with respect to concepts describing the relationship. Additionally, we exploit the relationships between topological features and the labels by statistical co-occurrence analysis. Finally, we demonstrate our three-layered semantic space with some examples.