Innovations are adopted by individuals and spread to other individuals. They are adopted at different rates, some are never adopted at all, some are abandoned, and some become the new norms. A very extensive evidence-based research and practice paradigm that studies how innovations spread is called diffusion of innovations. This scope of research is applicable to complex contexts of networks. During the past decades, there has been an enormous improvement in the eld with the advance of computational technology and understanding of social networks. Recently, researchers have brought their attention to more complex structures of networks such as multi-layered networks and temporal networks. However, it is still unclear about how innovation spreads in particular complex networks called multipartite networks. A multipartite network is a network whose nodes are partitioned into k different independent sets. The nodes in each set have edges that are only connected to the other k -1 sets. In this dissertation, I use a rigorous mathematical approach validated with different datasets to study how innovations are adopted in different communities that form multipartite networks.

Creator

• Lee, Hyojun Ada

DOI

• https://doi.org/10.21985/n2-xbfn-e794

Subject

• Statistics

Language

• en

Alternate Identifier

• http://dissertations.umi.com/northwestern:14765
• etdadmin_upload_678904

Keyword

• multipartite networks
• complex networks
• diffusion of innovations
• network reconstruction

Date created

• 2019-01-01

Resource type

• Dissertation

Rights statement

• In Copyright