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

Dörpinghaus, Jens; Klante, Sonja; Martin, Christian; Meigen, Christof; Düing, Carsten

doi:10.1016/j.ssaho.2022.100337

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…These issues may be due to the fact that several aspects of knowledge graphs and the semantic web are not widely perceived in the SNA community. They have only recently been brought together [23]. Barats et al conclude in 2020: FAIR data, a topic directly related to knowledge graphs, "remains a theoretical discussion rather than a shared practice in the field of humanities and social sciences."…”

Section: Related Workmentioning

confidence: 99%

“…The initial definition of a social network in [23] corresponds to the definition of a knowledge graph. In particular, the categories for nodes C 1 , ..., C n and edges R 1 , ..., R m can be modelled using RDF classes.…”

Section: A Modelling Longitudinal Social Networkmentioning

confidence: 99%

“…Thus, any social network according to the knowledge graph definition in [23] can be easily transformed into a temporal social network, where time is modelled as a property of nodes and edges. [4,6], t(e 1 ) = [3,4] and t(e 2 ) = [4,4].…”

Section: A Modelling Longitudinal Social Networkmentioning

confidence: 99%

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Towards modelling and analysis of longitudinal social networks

Dörpinghaus,

Weil,

Sommer

2023

Annals of Computer Science and Information Systems

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There are currently several approaches to managing longitudinal data in graphs and social networks. All of them influence the output of algorithms that analyse the data. We present an overview of limitations, possible solutions and open questions for different data schemas for temporal data in social networks, based on a generic RDF-inspired approach that is equivalent to existing approaches. While restricting the algorithms to a specific time point or layer does not affect the results, applying these approaches to a network with multiple time points requires either adapted algorithms or reinterpretation. Thus, with a generic definition of temporal networks as one graph, we will answer the question of how we can analyse longitudinal social networks with centrality measures. In addition, we present two approaches to approximate the change in degree and betweenness centrality measures over time.

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Section: Related Workmentioning

confidence: 99%

Section: A Modelling Longitudinal Social Networkmentioning

confidence: 99%