Communications of the ACM
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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As more and more graph data become available in various application domains, graph mining is of ever increasing importance in data management.Graph kernels are a novel and successful method for data mining in graphs. Unfortunately, implementing graph kernels is not trivial, and few applied researchers have therefore used graph kernels so far. In this demonstration, we present a Java software package called Visual Graph Mining (VGM). VGM allows the user to classify graphs using graph kernels and Support Vector Machines in a graphical user interface that is easy to learn and use. It is linked to LIBSVM for Support Vector Machine computations, yet can be easily transferred to other Support Vector Machine packages. Furthermore, VGM provides basic data mining features such as Nearest Neighbor search, graph algorithms such as Dijkstra, Floyd-Warshall, and computes and visualizes product graphs and topological indices of graphs.VGM 's homepage can be found at: http://www.cip.ifi.lmu.de/~boettger/sigmod.