A Simple Baseline Algorithm for Graph Classification
This provides a baseline algorithm for graph classification that is simpler and faster than existing complex methods, though it is incremental in nature.
The authors tackled the problem of graph classification by proposing a simple and fast algorithm based on spectral decomposition of graph Laplacians, achieving competitive results compared to state-of-the-art methods.
Graph classification has recently received a lot of attention from various fields of machine learning e.g. kernel methods, sequential modeling or graph embedding. All these approaches offer promising results with different respective strengths and weaknesses. However, most of them rely on complex mathematics and require heavy computational power to achieve their best performance. We propose a simple and fast algorithm based on the spectral decomposition of graph Laplacian to perform graph classification and get a first reference score for a dataset. We show that this method obtains competitive results compared to state-of-the-art algorithms.