frareba / embassi

Source code for the paper: Bause, F., Schubert, E. & Kriege, N.M. EmbAssi: embedding assignment costs for similarity search in large graph databases. Data Min Knowl Disc (2022). https://doi.org/10.1007/s10618-022-00850-3

EmbAssi

Source code for the paper: Bause, F., Schubert, E. & Kriege, N.M. EmbAssi: embedding assignment costs for similarity search in large graph databases. Data Min Knowl Disc (2022). https://doi.org/10.1007/s10618-022-00850-3

Usage

The algorithms contained in this package for searching in graph databases can be executed via a command line interface. Edit the shell script embAssi_gurobi, to reflect your Gurobi setup and run it (or use the script embAssi) to see a list of all available methods and parameters.

Gurobi and a Gurobi license are needed for computation of the exact graph edit distance (only used in the comparison of the various approximations (-cA)).

The results are written to results.txt.

Required parameters:
-d, --dataset Name of dataset
-m, --method Method to be used
Possible methods are:

• CLB (EmbAssi with clb)
• CStar
• Branch
• SLF
• LinD

A thorough description of these methods can be found in our paper and the corresponding references.

Optional parameters:
-clb, --clb Use CLB in a prefiltering step (Acceleration with EmbAssi)
-cA, --compareApprox Compare different approximations (Gurobi is needed for this)
-knn, --knnSearch Change type of query to knn
-qn, --queryNumber Number of queries (Default: 50)
-qr, --queryRange Maximum Range or k, depending on the query type (Default: 5)
-r, --raw Does the dataset have disconnected graphs, edge labels or any attributes?

Examples

The following command accelerates the method "CStar" using EmbAssi, making the filtering step for 50 range queries on the graphs in the dataset MUTAG for thresholds 1 to 5:

./embAssi -d MUTAG -m CStar -clb

The following command makes the filtering step for 50 range queries on the graphs in the dataset MUTAG for thresholds 1 to 5, using only the method "CStar", without acceleration:

./embAssi -d MUTAG -m CStar

Datasets

The repository contains the dataset MUTAG only. Further data sets in the required format are available from the website TUDatasets: A collection of benchmark datasets for graph classification and regression.. The dataset Protein_Complexes can be found here and Chembl_27 here.

Please note that edge labels, as well as attributes, are currently not supported.
Important:
If the dataset has disconnected graphs, edge labels or any attributes, these have to be removed first (using -r or --raw will generate a dataset with maximum of one discrete label per vertex and only connected graphs (keeping the largest connected component of each graph)). A preprocessed dataset (called dsName_p) will be generated and used for the experiments.

Terms and conditions

When using our code please cite our paper EmbAssi: embedding assignment costs for similarity search in large graph databases: Bause, F., Schubert, E. & Kriege, N.M. EmbAssi: embedding assignment costs for similarity search in large graph databases. Data Min Knowl Disc (2022). https://doi.org/10.1007/s10618-022-00850-3

References

EmbAssi uses an index provided by ELKI and the verification algorithm of BSS-GED:

• Erich Schubert and Arthur Zimek: ELKI: A large open-source library for data analysis. CoRR arXiv 1902.03616.

• Xiaoyang Chen, Hongwei Huo, Jun Huan, and Jeffrey S. Vitter. An Efficient Algorithm for Graph Edit Distance Computation. Knowledge-Based Systems, 2019, 163: 762–775

Contact information

If you have any questions, please contact Franka Bause.