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Graph Kernels for Data Mining

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If you have a question about this talk, please contact Carl Edward Rasmussen .

As new graph structured data is constantly being generated, learning and data mining on graphs have become a challenge in application areas such as molecular biology, telecommunications, chemoinformatics, and social network analysis. The central algorithmic problem in these areas, measuring similarity of graphs, has therefore received extensive attention in the recent past. Unfortunately, existing approaches are slow, lacking in expressivity, or hard to parameterize.

Graph kernels have recently been proposed as a theoretically sound and promising approach to the problem of graph comparison. Their attractivity stems from the fact that by defining a kernel on graphs, a whole family of data mining and machine learning algorithms becomes applicable to graphs.

These kernels on graphs must respect both the information represented by the topology and the node and edge labels of the graphs, while being efficient to compute. Existing methods fall woefully short; they miss out on important topological information, are plagued by runtime issues, and do not scale to large graphs.

In this talk, we will present our work on solving these problems, and our novel graph kernels and kernel methods for mining large graphs and large datasets of graphs.

This talk is part of the Machine Learning @ CUED series.

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