See Archive

29 August 2019 on Interconnectedness in Digital Trace Data Contexts

Title:  Interconnectedness in Digital Trace Data Contexts

Abstract :

This talk is about how to apply “Network Science” concepts to Interconnectedness in Digital Trace Data Contexts including online health platforms, low-code software development platforms, car accidents, best friendship. The basic trust of this talk is that the very nature of these contexts, which are often supported by online platforms, can be best described as a network of entangled interactions, that is interconnectedness. We agree with scholars that this necessitates the call for theory of network as a novel approach to better understand their underpinnings. We will give examples from different real-world events and digital trace data, managerial decision elements. We will demonstrate that the network representing interconnectendess exhibits essential structural properties that characterize most real networks. We further discuss that dynamic network analysis helps us to observe how network structure and components have evolved over time and to identify a particular pattern towards emerging a giant component. We introduce a novel analytics approach for relating digital trace data to interconnectedness analytics by adopting the Network Science concepts and models. We propose interconnectedness analytics as novel and complementary metrics to those used in conventional analytics. 


By:

Mehmet N. Aydin, PhD
Assoc.Prof. of MIS
Management Information Systems Department
Kadir Has University, Istanbul

https://www.khas.edu.tr/en