In our experiments, all the code implements with C++ code.
Link: SourceCode/OurMethods
- 1-EIS: K-EIS for single target.
- K-EIS: K-EIS for multiple target.
- Mix-EIS: Improved K-EIS for multiple target based on subgraph partition.
- Mix-EIS-IGS: Mix-EIS includes a single-target searching algorithm, using IGS to replace 1-EIS for comparison.
Link: SourceCode/CompareMethods
- IGS: A single-target search method over DAGs.
- TS-IGS: Improved IGS.
- BinG: A single-target search method over Tree.
- KBM-IGS1: A single-target search method over Tree.
- KBM-IGS2: A multiple-targets search method over Tree.
Link: Datasets&EXP
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ProductClassification: Tree.
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Amazon: Tree.
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ACM_CCS: DAG.
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Wiki_Edits: DAG.
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ImageNet: DAG.
Format:
The first line with two integer
The next
Put the source code into same folder with datasets, testcase and run.sh.
./run.sh
Single-target :
link: Datasets&EXP/SingleTargetTestCase
Multiple-targets :
link: Datasets&EXP/MultipleTargetsTestCase
For each folder K = x
, x represents the number of the hidden targets.