Graph embedding using multi-layer adjacent point merging model
This work addresses graph classification tasks where class depends on subgraph patterns, offering an incremental improvement over existing methods.
The authors tackled the problem of graph classification by proposing a novel graph embedding method that extracts subgraph patterns and uses a flexible loss function for feature selection, resulting in outperforming many state-of-the-art methods in numerical evaluations.
For graph classification tasks, many traditional kernel methods focus on measuring the similarity between graphs. These methods have achieved great success on resolving graph isomorphism problems. However, in some classification problems, the graph class depends on not only the topological similarity of the whole graph, but also constituent subgraph patterns. To this end, we propose a novel graph embedding method using a multi-layer adjacent point merging model. This embedding method allows us to extract different subgraph patterns from train-data. Then we present a flexible loss function for feature selection which enhances the robustness of our method for different classification problems. Finally, numerical evaluations demonstrate that our proposed method outperforms many state-of-the-art methods.