Graphlet-based lazy associative graph classification
This work addresses graph classification for domains like predictive toxicology, but it appears incremental as it modifies an existing method.
The paper tackles the graph classification problem by modifying a lazy associative classification method to efficiently handle graph intersections, approximating them with common subgraphs up to a fixed size, and reports experiments on a predictive toxicology dataset.
The paper addresses the graph classification problem and introduces a modification of the lazy associative classification method to efficiently handle intersections of graphs. Graph intersections are approximated with all common subgraphs up to a fixed size similarly to what is done with graphlet kernels. We explain the idea of the algorithm with a toy example and describe our experiments with a predictive toxicology dataset.