Graph of Graphs: A New Knowledge Representation Mechanism for Graph Learning (Student Abstract)

Abstract

Supervised graph classification is one of the most actively developing areas in machine learning (ML), with a broad range of domain applications, from social media to bioinformatics. Given a collection of graphs with categorical labels, the goal is to predict correct classes for unlabelled graphs. However, currently available ML tools view each such graph as a standalone entity and, as such, do not account for complex interdependencies among graphs. We propose a novel knowledge representation for graph learning called a {\it Graph of Graphs} (GoG). The key idea is to construct a new abstraction where each graph in the collection is represented by a node, while an edge then reflects similarity among the graphs. Such similarity can be assessed via a suitable graph distance. As a result, the graph classification problem can be then reformulated as a node classification problem. We show that the proposed new knowledge representation approach not only improves classification performance but substantially enhances robustness against label perturbation attacks.