Lullabi: Knowledge Visualization Networks

Abstract

Network science is an ever-evolving field. As the research continues to evolve, new gaps are opened, and tools are created to help fill these gaps. There are a variety of network analysis tools available for research and they cover a wide range of use cases. However, there are three gaps to be addressed. First, there seems to be little discussion that network links can carry as much information (attributes) as nodes, and the important role that this information can play. Second, there is a need for multimedia data within networks, where images, video, sound and documents can be used to visually enhance the display of information in a network. Third, network partitions do not address the idea of multiple classifications, direct use of continuous data to create clusters, or clusters composed entirely of links. A Knowledge Visualization Network (KVN) is presented as an innovative approach to filling this gap.