Modularity-based approaches for nodes clustering in hypergraphs

Veronica Poda · Catherine Matias

Contains the scripts to perform the experiments from [1] on testing and comparing modularity-based approaches for nodes clustering in hypergraphs.

Requirements

To run those scripts and perform our experiments, you will need

• R and the package hyperSBM
• Python and the libraries hypernetx, igraph
• Julia and the libraries Clustering, SimpleHypergraphs, ABCDHypergraphGenerator

Synthetic models for modular hypergraphs

We generated modular hypergraphs where ground truth clusters are known through the 3 following models:

• HSBM: this model proposed in [6] relies on R package hyperSBM. Under this model, we generated scenarios A and C (see file Scenarios_HyperSBM.R)
• DCHSBM-like: this model proposed in [2] relies on the project Hypermodularity. Under this model, we generated scenarios A, B, D, E and F (see Scenarios_DCHSBM-like.jl file)
• h-ABCD: this model proposed in [7] relies on ABCDHypergraphGenerator. After installing this library, go to the directory ABCDHypergraphGenerator.jl-main/utils to run the commands below. Under this model, we generated scenarios A and Z as follows:
  • Scenarios A use the following command (changing the value of sample size n, the folder name scenA1, repetition number rep1 and the file same_size_50.txt that contains the sizes of the cluster depending on the total sample size n)

julia --project abcdh.jl -n 50 -d 2.07,1,32 -c same_size_50.txt	-x 0.37 -q 0.0,0.66,0.34 -w :strict -o "hABCD/scenA1/rep1"

• and
  • Scenarios Z use the following command (changing the value of sample size n, the folder name scenZ1 and repetition number rep1)

julia --project abcdh.jl -n 50 -d 2.5,1,10 -c 1.5,10,80	-x 0.5 -q 0.0,0.6,0.4 -w :linear -o "hABCD/scenZ1/rep1"

All the generated datasets are stored in directories of the form model/scenAi/ where model= HyperSBM or DCHSBM or hABCD and scenarios take different values (see [1] for the scenario's description).

Methods

Our scripts use the following methods:

• AON-HMLL: This is the method from [2], whose implementation can be found at the project Hypermodularity. To run the script, simply use a command like

julia AON_HMLL_script.jl -m "HyperSBM/" -s "scenA1/" -n 25

• CNM-like: This is the method from [3], whose implementation can be found at the project SimpleHypergraphs. To run the script, simply use a command like

julia CNM_like_script.jl -m "DCHSBM/" -s "scenA1/" -n 25

• IRMM: This is the method from [4], whose implementation can be found at the HyperNetx project. To run the script, simply use a command like

python IRMM_script.py -m "HyperSBM/" -s "scenA2/" -n 25

• LSR: This is the method from [5], whose implementation can also be found at the HyperNetx project. To run the script, simply use a command like

python LSR_script.py -m "HyperSBM/" -s "scenA5/" -n 25

References:

1. Poda, V. and Matias, C. (2024). Comparison of modularity-based approaches for nodes clustering in hypergraphs. Submitted. Preprint
2. Chodrow, P. S., N. Veldt, and A. R. Benson (2021). Generative hypergraph clustering: From blockmodels to modularity. Science Advances 7(28), eabh1303 Journal link
3. Kamiński, B., V. Poulin, P. Prałat, P. Szufel, and F. Théberge (2019a). Clustering via hypergraph modularity. PLoS ONE 14(11), e0224307. Journal link.
4. Kumar, T., S. Vaidyanathan, H. Ananthapadmanabhan, S. Parthasarathy, and B. Ravindran (2020). Hypergraph clustering by iteratively reweighted modularity maximization. Appl. Netw. Sci. 5(1), 52. Journal link
5. Kamiński, B., P. Prałat, and F. Théberge (2021). Community detection algorithm using hypergraph modularity. In R. M. Benito, C. Cherifi, H. Cherifi, E. Moro, L. M. Rocha, and M. Sales-Pardo (Eds.), Complex Networks & Their Applications IX, pp. 152–163. Manuscript link
6. Brusa, L. and C. Matias (2022). Model-based clustering in simple hypergraphs through a stochastic blockmodel. ArXiV preprint.
7. Kamiński, B., P. Prałat, and F. Théberge (2023). Hypergraph artificial benchmark for community detection (h-abcd). ArXiV preprint.