Building Multiple Classifier Systems Using Linear Combinations of Reduced Graphs.

Despite great efforts done in research in the last decades, the classification of general graphs, i.e., graphs with unconstrained labeling and structure, remains a challenging task. Due to the inherent relational structure of graphs it is difficult, or even impossible, to apply standard pattern recognition methods to graphs to achieve high recognition accuracies. Common methods to solve the non-trivial problem of graph classification employ graph matching in conjunction with a distance-based classifier or a kernel machine. In the present paper, we address the specific task of graph classification by means of a novel framework that uses information acquired from a broad range of reduced graph subspaces. Our novel approach can be roughly divided into three successive steps. In the first step, differently reduced graphs are created out of the original graphs relying on node centrality measures. In the second step, we compute the graph edit distance between each reduced graph and all the other graphs of the corresponding graph subspace. Finally, we linearly combine the distances in the third step and feed them into a distance-based classifier to obtain the final classification result. On six graph data sets, we empirically confirm that the proposed multiple classifier system directly benefits from the combined distances computed in the various graph subspaces

Solving the Graph Edit Distance Problem with Variable Partitioning Local Search

International audienceIn the world of graph matching, the Graph Edit Distance (GED) problem is a well-known distance measure between graphs. It has been proven to be a NP-hard minimization problem. This paper presents an adapted version of Variable Partitioning Local Search (VPLS) matheuristic for solving the GED problem. The main idea in VPLS is to perform local searches in the solution space of a Mixed Integer Linear Program (MILP). A local earch is done in a small neighborhood defined based on a set of special variables. Those special variables are selected based on a procedure that extracts useful characteristics from the instance at hand. This actually ensures that the neighborhood contains high quality solutions. Finally, the experimentation results have shown that VPLS has outperformed existing heuristics in terms of solution quality on CMUHOUSE database