Synthetic Networks For Benchmarking

The synthetic networks in this dataset were generated using the RECCS protocol developed by Anne et al. (2024). Briefly, the RECCS process is as follows. An input network and clustering (by any algorithm) is used to pass input parameters to a stochastic block model (SBM) generator. The output is then modified to improve fit to the input real world clusters after which outlier nodes are added using one of three different options. See Anne et al. (2024): in press Complex Networks and Applications XIII (preprint : arXiv:2408.13647).

The networks in this dataset were generated using either version 1 or version 2 of the RECCS protocol followed by outlier strategy S1. The input networks to the process were (i) the Curated Exosome Network (CEN), Wedell et al. (2021), (ii) cit_hepph (https://snap.stanford.edu/), (iii) cit_patents (https://snap.stanford.edu/), and (iv) wiki_topcats (https://snap.stanford.edu/).

Input Networks:
The CEN can be downloaded from the Illinois Data Bank:
https://databank.illinois.edu/datasets/IDB-0908742 -> cen_pipeline.tar.gz -> S1_cen_cleaned.tsv

The synthetic file naming system should be interpreted as follows: a_b_c.tsv.gz where
a - name of inspirational network, e.g., cit_hepph
b - the resolution value used when clustering a with the Leiden algorithm optimizing the Constant Potts Model, e.g., 0.01
c- the RECCS option used to approximate edge count and connectivity in the real world network, e.g., v1

Thus, cit_hepph_0.01_v1.tsv indicates that this network was modeled on the cit_hepph network and RECCSv1 was used to match edge count and connectivity to a Leiden-CPM 0.01 clustering of cit_hepph. For SBM generation, we used the graph_tool software (P. Peixoto, Tiago 2014. The graph-tool python library. figshare. Dataset. https://doi.org/10.6084/m9.figshare.1164194.v14)

Additionally, this dataset contains synthetic networks generated for a replication experiment (repl_exp.tar.gz). The experiment aims to evaluate the consistency of RECCS-generated networks by producing multiple replicates under controlled conditions. These networks were generated using different configurations of RECCS, varying across two versions (v1 and v2), and applying the Connectivity Modifier (CM++, Ramavarapu et al. (2024)) pre-processing. Please note that the CM pipeline used for this experiment filters small clusters both before and after the CM treatment.

Input Network : CEN

Within repl_exp.tar.gz, the synthetic file naming system should be interpreted as follows:
cen_sample.tsv

where:
cen – Indicates the network was modeled on the Curated Exosome Network (CEN).
resolution – The resolution parameter used in clustering the input network with Leiden-CPM (0.01).
cm_status – Either cm (CM-treated input clustering) or no_cm (input clustering without CM treatment).
reccs_version – The RECCS version used to generate the synthetic network (v1 or v2).
replicate_id – The specific replicate (ranging from 0 to 2 for each configuration).

For example:
cen_0.01_cm_v1_sample_0.tsv – A synthetic network based on CEN with Leiden-CPM clustering at resolution 0.01, CM-treated input, and generated using RECCSv1 (first replicate).
cen_0.01_no_cm_v2_sample_1.tsv – A synthetic network based on CEN with Leiden-CPM clustering at resolution 0.01, without CM treatment, and generated using RECCSv2 (second replicate).

The ground truth clustering input to RECCS is contained in repl_exp_groundtruths.tar.gz.