How Hyper-Network Analysis Helps Understand Human Networks?

Abstract

Service enterprises are value cocreation networks. From this perspective, we analyze how hyper-network models can lead to new understanding for service science. A hyper-network is an integration of multi-layered (role-based) connections of members in a community, such as the Internet and an ecosystem. Hyper-network analysis provides new results of multi-dimensional understanding of human networks, such as identifying the centers of connections between layers (the hyper-hubs or “value wormholes”) and the shortened distance between nodes (or, the centrality of members) due to such value wormholes. This paper shows that the common practices of adding new links to an existing random graph (e.g., merging a Website with other social networking/ecommerce sites) is equivalent to creating a hyper-network; and hyper-network analysis may reveal otherwise hidden social structures and thereby yield more accurate estimates for network performance. Estimation formulae are provided for determining the average vertex-vertex distance and average vertex degree. On this basis, the paper proves that hyper-networks enhance ordinary random graphs in these measures, and hence can probably model real-world social networks better than two-dimensional graphs. The paper suggests that all human networks, including social and economical, may be fundamentally hyper-networks. [Service Science, ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3.]