Multi-class instance-incremental framework for classification in fully dynamic graphs

Existing work in the area of graph classification is mostly restricted to static graphs. These static classification models prove ineffective in several real life scenarios that require an approach capable of handling data of a dynamic nature. Further, the limited work in the domain of dynamic graphs mainly focuses on solely incremental graphs which fail to accommodate fully dynamic graphs (FDG). Hence, in this paper, we propose a comprehensive framework targeting multi-class classification in fully dynamic graphs by utilising the efficient Weisfeiler-Lehman graph kernel (W-L) with a multi-class support vector machine (SVM). The framework iterates through each update using the instance-incremental method while retaining all historical data in order to ensure higher accuracy. Reliable validation metrics are utilised for the model parameter selection and output verification. Experimental results over four case studies on real-world data demonstrate the efficacy of our approach.