Understanding large network datasets through embodied interaction in virtual reality

Abstract

The intricate web of information we generate nowadays is more massive than ever in the history of mankind. The sheer enormity of big data makes the task of extracting semantic associations out of complex networks more complicated. Stemming this "data deluge" calls for novel unprecedented technologies. In this work, we engineered a system that enhances a user's understanding of large datasets through embodied navigation and natural gestures. This system constitutes an immersive virtual reality environment called the "eXperience Induction Machine" (XIM). One of the applications that we tested using our system is the exploration of the human connectome: the network of nodes and connections that underlie the anatomical architecture of the human brain. As a comparative validation of our technology, we then exposed participants to a connectome dataset using both our system and a state-of-the-art software for visualization and analysis of the same network. We systematically measured participants' understanding and visual memory of the connectomic structure. Our results showed that participants retained more information about the structure of the network when using our system. Overall, our system constitutes a novel approach in the exploration and understanding of large complex networks.