D-iteration: evaluation of the update algorithm
For researchers working with dynamic graphs, this work offers an incremental improvement in computational efficiency for matrix-vector operations.
The paper analyzes the D-iteration update algorithm, a fluid diffusion-based iterative method for matrix-vector products, and demonstrates through experiments on real datasets that it improves computation efficiency for evolving graphs.
The aim of this paper is to analyse the gain of the update algorithm associated to the recently proposed D-iteration: the D-iteration is a fluid diffusion based new iterative method. It exploits a simple intuitive decomposition of the product matrix-vector as elementary operations of fluid diffusion (forward scheme) associated to a new algebraic representation. We show through experimentations on real datasets how much this approach can improve the computation efficiency in presence of the graph evolution.