On Classifying Dynamic Graph Bags

Abstract

In this paper, we introduce a novel problem of dynamic graph bag classification, and propose a method to solve this problem. Here, a graph bag (simply, bag) corresponds to a training object that contains one or multiple graphs. Dynamic bag classification aims to build a classification model for bags which are presented in a dynamic fashion, i.e., emerging of new bags or graphs. Our proposed solution for this problem can gradually update the classification model whenever such changes are made to a bag dataset, rather than building a model from the scratch. We demonstrate the effectiveness of our proposed method by our extensive evaluation on a real-world graph dataset.