Efficient Algorithms for the Group Steiner Tree Problem with Many Groups

This is the source code of the paper 'Efficient Algorithms for the Group Steiner Tree Problem with Many Groups'.

Table of contents

• Environment
• Data and Compile Command
• Experiment 1 : KG Summarization
• Experiment 2 : VLSI Design
• Experiment 3 : Keyword Search over KGs
• Experiment 4 : Parameter Study
• • Experiment 4.1 parameter study on PCSG
  • Experiment 4.2 parameter study on VLSI
• Citation

Environment

C++ (need to support C++11)

Python 3

600G Memory

Data and Compile Command

Our datasets is available on datasets_part1, datasets_part2 and dataset_part3.

If you want to run our algorithms on a new graph, please build Hub Labeling following KeyKG by yourself.

The compile command for our C++ source codes is shown below: for A.cpp, we use g++ A.cpp -o A -std=c++11 to compile it.

Experiment 1: KG Summarization

This experiment is based on PCSG.

We provide the input we need in the dataset.

Our source code is in src/PCSG.

For GSTGrow, GSTMerge and KeyKG:

You need to compile src/PCSG/GSTGrow.cpp ,src/PCSG/GSTMerge.cpp and src/PCSG/KeyKG.cpp first.

Then, you need to put the executable file, newhublabel.txt and invertedTable.txt into a folder. These dataset are in PCSG_dataset.zip on our datasets.

Now, you can run the executable file to get a result.

For PartialOPT:

You need to compile src/PCSG/PartialOPT.cpp first.

Then, you need to use the original graph in original_graph.zip, under the /PCSG folder. Please put the graph into this folder, and run the executable file.

If you want to run our algorithms on more KGs, please following the code in PCSG to get the HubLabel and invertedTable. Then, you can use src/PCSG/change_hublabel.cpp to transform the hublabel into the new hublabel (which records the address of the precursor of each hub).

Experiment 2: VLSI Design

The source code of this experiment is in src/VLSI.

Please compile auto.cpp,gen.cpp,GSTGrow.cpp,GSTMerge.cpp,KeyKG.cpp,ImprovAPP.cpp,PartialOPT.cpp.

Then use ./auto to get the result. The result is in table.txt.

Experiment 3: Keyword Search over KGs

The source code of this experiment is in src/KeywordSearch.

For KeyKG, GSTGrow and GSTMerge:

You should download the dataset from KeywordSearch_dataset.zip in our datasets, then put WeightPLLlabel.txt, kwName.txt, kwMap.txt and query.txt into the source code folder.

First, please compile and use change_hublabel.cpp to change WeightPLLlabel.txt to newhublabel.txt.

Then, please use query_change.py to change kwName.txt, kwMap.txt and query.txt to newquery.txt.

Next, please compile and run GSTGrow.cpp, GSTMerge.cpp and KeyKG.cpp.

For ImprovAPP and PartialOPT:

First, compile the ImprovAPP_iw.cpp,ImprovAPP_uw.cpp, PartialOPT_iw.cpp,PartialOPT_uw.cpp.

You should download the dataset from original_graph.zip, and find the /KeywordSearch folder. For the graph with iw and uw, please put it and the corresponding executable file into one folder. Then, run the executable file.

We record the exact solution in /exact_result. There are 4 files DBpedia_iw.txt, DBpedia_uw.txt, LinkedMDB_iw.txt, LinkedMDB_uw.txt . Put the exact solution file, KeyKG_result.txt, Grow_result.txt and Merge_result.txt , ImprovAPP_result.txt and PartialOPT_result.txtinto a folder, and change the exact solution file name to exact_result.txt, then compile calc.cpp , calc2.cpp and run them. You can get result files count.txt and count2.txt, and they will show you the approximation ratio.

Experiment 4: Parameter Study

Experiment 4.1 Parameter Study on PCSG

The source code is in src/parameter/PCSG.

Please download the newhublabel.txt and invertedTable.txt of ID from PCSG_dataset.zip in our datasets.

Then compile and run GSTGrow.cpp to get the result.

Next adjust the global variable tau from 1 to 9 in GSTMerge.cpp, compile and run it to get the result of different $\tau$.

You can calculate the ratio by the results.

Experiment 4.2 Parameter Study on VLSI

The source code is in src/parameter/VLSI.

Please compile all C++ source code, and use ./auto to get the result. The result is in table_tau.txt.

Citation

If you think our algorithms or our experimental results are useful, please kindly cite our paper.