Deep Learning on Graphs: Directed Graphs, Edge Structures and Graph Estimation
Michael P. Kenning · 2023
In the last decade and a half, machine learning has been refounded on a class of techniques called deep learning.The earliest, most prominent techniques of deep learning were restricted in their application to regularly structured domains.A new set of techniques, broadly referred to as geometric deep learning, extends the application of deep learning approaches to irregular domains, in particular the use of the graph.A graph is an effective means of representing irregular relations between discretely sampled points; its use has its attendant research challenges that has brought about a flourishing field of research.In this work we investigate three of those challenges, namely learning on directed graphs, one of the many variants of the graph; learning on the edge-structure of graphs; and graph estimation, i.e. the estimation of graph structure from the data itself.In the first chapter of our work, we consider the challenge of learning on the edge structure of a graph in application to a datacentre and present a convolution technique for the edge-structure of a directed graph representing a datacentre.