Chapter

Structural, Syntactic, and Statistical Pattern Recognition

Volume 5342 of the series Lecture Notes in Computer Science pp 287-297

IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning

  • Kaspar RiesenAffiliated withInstitute of Computer Science and Applied Mathematics, University of Bern
  • , Horst BunkeAffiliated withInstitute of Computer Science and Applied Mathematics, University of Bern

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Abstract

In recent years the use of graph based representation has gained popularity in pattern recognition and machine learning. As a matter of fact, object representation by means of graphs has a number of advantages over feature vectors. Therefore, various algorithms for graph based machine learning have been proposed in the literature. However, in contrast with the emerging interest in graph based representation, a lack of standardized graph data sets for benchmarking can be observed. Common practice is that researchers use their own data sets, and this behavior cumbers the objective evaluation of the proposed methods. In order to make the different approaches in graph based machine learning better comparable, the present paper aims at introducing a repository of graph data sets and corresponding benchmarks, covering a wide spectrum of different applications.