Exploiting Graph Matrix Duality for Efficient Graph Data Processing

Linear algebra is the backbone of modern applied data analytics. Not only does it provide the theoretical substratum for the development of analytical algorithms it also lends the technological support for extremely efficient implementations. The linear algebra ecosystem consists of remarkably fast hardware support with equally efficient software solutions leveraging the hardware capacity. Graphs are an ubiquitous modelling framework for the representation and analysis of connected data.A flipside from practical perspective is that conventional graph algorithms are challenging as they are hard to parallelize and often make too much use of non-linear data structures which do not have hardware support. In this context, this paper presents an invitation to an alternate way to look at graph processing in terms of algebraic constructs, formulations and technology. The paper presents the theoretical underpinnings of the new view and also some interesting progresses at the frontier level in this direction. Above all, an intended objective is to present a compelling invite to the academia and research community to adopt this approach in their academic and research activities.