IAM Graph Database Repository

Overview

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 website aims at introducing a repository of graph data sets and corresponding benchmarks, covering a wide spectrum of different applications.

Note that other benchmarking data sets for graph matching can be found on the TC-15 website.

Currently there are 100 registered users of this database repository.

 

Characteristics

The IAM Graph Database Repository contains:

  • Letter Graphs
  • Digit Graphs
  • GREC Graphs
  • Fingerprint Graphs
  • COIL Graphs (2 versions)
  • Webpage Graphs
  • AIDS Graphs
  • Mutagenicity Graphs
  • Protein Graphs

A summary of the main characteristics of all data sets can be found here.

Download

Before you can download the IAM Graph DB repository we ask you to register so we are aware of who is using our data. Once you have registered you can access the IAM Graph DB repository. If you are publishing scientific work based on the IAM-Graph DB, we request you to include a reference to our paper Riesen, K. and Bunke, H.: IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning. In: da Vitora Lobo, N. et al. (Eds.), SSPR&SPR 2008, LNCS, vol. 5342, pp. 287-297, 2008.

Terms of Use

This database may be used for non-commercial research purpose only. 

Contact

If you have any questions or suggestions, please contact Kaspar Riesen.

Document Actions