Linked data for network science

Abstract

Network science is an emerging research area focused on developing general network-based approaches for studying phenomena across a range of fields from social science to biology. Techniques from network science include network analysis, network modeling and visualization. A key difficulty facing networks science is data acquisition. Network data must often be mined and converted from non-network sources, which is often a laborious and error prone process. In this paper, we present a simplified approach for extracting networks from Linked Data. These extracted networks can then be analyzed through network analysis algorithms, and the results of these analyses can be published back as Linked Data. The aim is to provide a corpus of well-described networks for use in network science. We describe LinkedDataLens, an implementation of this framework that uses the Wings workflow system to represent multi-step network extraction and analysis processes. Additionally, we describe initial networks that have been extracted and characterized with this framework.