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The ParClusterers Benchmark Suite (PCBS)

Compilation

To compile the clusterers:

git submodule init
git submodule update

# compile the clustering algorithms
bazel build //clusterers:cluster-in-memory_main
# compile the stats computation
bazel build //clusterers:stats-in-memory_main

We use the bazel build system. Installation instructions can be foun here. We recommend bazel version >= 6.2.1.

Required python packages are listed in requirements.txt. Some packages are only required for benchmarking implementations from other libraries, such as NetworKit, Neo4j, and TigerGraph.

Input

Input can be in either edge list format or GBBS (CSR) format. SNAP edge list format can be converted to GBBS format using GBBS.

# In GBBS
bazel run //utils:snap_converter -- -s -i com-friendster.ungraph.txt -o com-friendster.gbbs.txt

Quick Start

The commands below runs clustering algorithms on the two graphs in data/ and compute stats on the resulting clusterings.

pip3 install -r requirements.txt
python3 cluster.py cluster.config
python3 stats.py cluster.config stats.config

Configuration Files

cluster.config

This config specifies what clustering algorithms to run, along with the corresponding config proto for each algorithm. The script will run all combinations of the specified config protos, on all given graphs.

Iutput directory: the directory where Graphs files are located. The directory path must be an absolute path.

Output directory: where the clustering files and log fiels will be stored. The directory path must be an absolute path.

CSV output directory after cluster.py and stats.py are run, a runtime.csv and stats.csv file will be automatically generated into this directory. The directory path must be an absolute path.

postprocess only: If it is set to true, when cluster.py or stats.py are run, thhey only generate the csv files from the output files, but do not re-compute the clustering/stats again.

GBBS format: Whether the input graphs are in edge list format or gbbs format. Only native PBCS methods can read GBBS format. Implementations from other libraries can only read edge list format in our benchmarking suite.

Note that the lines in configurations files should not have dangling ; at the end.

For example:

Input directory: /home/ubuntu/ParClusterers/data/
Output directory: /home/ubuntu/ParClusterers/out/
CSV Output directory: /home/ubuntu/ParClusterers/out_csv/
Clusterers: ConnectivityClusterer;ParallelCorrelationClusterer
Graphs: iris.graph.txt;digits.graph.txt
GBBS format: false
Weighted: true
Number of threads: 10; ALL
Number of rounds: 1
Timeout: 7h
Postprocess only: false

ParallelCorrelationClusterer:
  correlation_clusterer_config:
    resolution: 0; 0.1;0.3; 0.5; 0.7; 0.9; 1
    louvain_config: {num_iterations: 10, num_inner_iterations: 10}; {num_iterations: 20, num_inner_iterations: 20}
    use_refinement: true; false
    clustering_moves_method: LOUVAIN

ConnectivityClusterer:
  connectivity_config:
    threshold: 0.50; 0.98
    upper_bound: false

stats.config

This config specifies what statistics to compute, given that you have already run a set of clustering algorithms using cluster.config. Like cluster.config, the stats.config parses the set of flags desired at the top of the file, and the stats config protos at the bottom of the file, and runs all combinations, which are then stored into the same output directory as specified by cluster.config.

Deterministic: if it’s true, the stats will only be computed for a single round and a single thread number, since the clustering algorithm is deterministic, the stats should be the same for all rounds and all thread numbers.

For example:

Input communities: iris.cmty.txt; digits.cmty.txt
Deterministic: false

statistics_config:
  compute_precision_recall: true
  f_score_param: 0.5

Compatibility with other libraries

PCBS supports benchmarking methods from Neo4j, NetworKit, and TigerGraph. Current implementations are tested with networkit version 11.0, Neo4j community version 5.19.0 with graph data science library version 2.6.5 and TigerGraph version 3.9.2.

Additional

Running original sequential Tectonic

If your clustering config file includes the original Tectonic, you also need the system.config file. This config lists where g++ and Python can be found. You can modify it to use your preferred compiler and python version. This is used for the original Tectonic, not our parallel Tectonic (TectonicClusterer). For example:

python3 cluster.py cluster.config system.config

Experiments

This repo contains the artifacts for the following paper:

Shangdi Yu, Jessica Shi, Jamison Meindl, Laxman Dhulipala, Sasan Tavakkol, Xiaoen Ju, Jakub Łącki, Vahab Mirrokni, and Julian Shun, The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering.

Please find instructions to download data and run experiments in configs_experiments/Experiment.md.