α-Indirect Control in Onion-like Networks

We propose a fast, accurate, and scalable algorithm to detect ultimate controlling entities in global corporate networks. α-ICON uses company-participant links to identify super-holders who exert control in networks with millions of nodes.

By exploiting onion-like properties of such networks we iteratively peel off the hanging vertices until a dense core remains. This procedure allows for a dramatic speed-up, uncovers meaningful structures, and handles circular ownership by design.

Read our paper with the applications. As a toy example, consider the below corporate network where α-ICON designates Mr Philip Mactaggart (in green) as the super-holder exerting control over all other entities, directly or indirectly held:
## Installation To replicate the analysis you need to clone this repository to your local machine. Then you need to install the required versions of R dependencies listed in `DEPENDENCIES`. `code/helper_functions/install_dependencies.r` automates this step, but you may still need to install the underlying libraries manually with [Homebrew](https://brew.sh) or `apt-get`, depending on your platform. Finally, you need to declare the environment variable `ALPHAICON_PATH` in bash pointing to the repository. Or, better yet, you can add it in your `.Renviron` with ```console user:~$ echo 'ALPHAICON_PATH="path_to_cloned_repository"' >> ~/.Renviron ``` The repository does not contain any data due to its size (10+ GB unpacked); most files in `data/` and `output/` folders are zero-byte placeholders. We provide a public Google Drive folder with the populated `data/` and `output/` directories. You may still need to unzip them manually. A self-contained example of α-ICON is also available in Google Colaboratory. ## Repository structure ``` data/ ├─uk/ # Data on UK companies and participants | ├ persons-with-significant-control-snapshot-2021-08-02.txt # Source PSC data | ├ BasicCompanyDataAsOneFile-2021-08-01.csv # Source data on live companies in UK | ├ sic_2007_code_list.csv # Standard Industrial Classification codes | ├ psc_snapshot_2021-08-02.rdata # Processed People with Significant Control data | └ uk_basic_companies_data_2021-08-01.rdata # Processed Basic Company data | ├─corpwatch_api_tables_csv_14aug21/ # Data from CorpWatch Dump | ├ company_info.csv # Source companies data from SEC filings | ├ cik_name_lookup.csv # Company name variants in SEC filings | └ company_locations.csv # Company locations in SEC filings | code/ ├─helper_functions/ | ├ install_dependencies.r # Installs R dependencies used in the project | └ compute_power_index.r # Computes Mizuno et al. (2020) DPI and NPI | ├─data_preparation/ | └─uk/ | ├ 1a_process_psc_snapshot.r # Prepare source PSC data | ├ 1b_process_companies_data.r # Prepare source data on live companies | ├ 2_psc_snapshot_to_participants_panel.r # PSC data to entity-participant info | └ 3_prepare_affiliated_entities_evaluation_data.r # Process CorpWatch data | ├─alphaicon_paper/ | ├ 1_compute_alphaicon.ipynb # Jupyter Notebook w. α-ICON (also on Google Colab) | ├ 2_compute_npi_dpi.r # Computation of Direct and Network Power Indices | ├ 3_summary_stat_by_node_type.r # UK PSC network statistics by core/SH/ST/I | ├ 4_illustrate_algorithm.r # Visualise selected networks | ├ 5_algorithm_evaluation.r # Compute recall @ k and l for various algorithms | └ 6_rank_top_holders.r # Examine the rankings of super-holders & Kendall's tau | output/ ├─uk/ | ├ uk_organisations_participants_2021_long_2aug21.csv # Primary ownership data | ├ uk_organisations_participation_graph_core_periphery_membership_6aug21.csv | ├─npi_dpi/ # Mizuno et al. (2020) computation results on UK PSC data | | └─10000iter/ | | ├ uk_organisations_participants_2021_long_7sep21_dpi_10000iter.csv # DPI | | └ uk_organisations_participants_2021_long_7sep21_npi_10000iter.csv # NPI | | | ├─transitive/ # Computed α-ICON shares on equity shares or DPI weights | | ├ uk_organisations_transitive_ownership_alpha*_2021_long_2aug21.csv # α = * | | └ uk_organisations_transitive_ownership_alpha*_2021_long_7sep21_dpi_....csv | | └─alphaicon_paper/ ├ uk_orgs_algorithm_evaluation_recall.csv # Algorithm recall by k ├ uk_orgs_algorithm_evaluation_recall_by_pathlength.csv # Algorithm recall by l ├ uk_organisations_top100_holders_2021_long_2aug21.csv # Top SH in PSC network ├ uk_organisations_top100_holders_diff_npi_dpi_2021_long_2aug21.csv # Top-100 SH | # with the largest difference betw. total DPI and NPI ├ uk_organisations_top100_holders_diff_transitive_dpi_2021_long_2aug21.csv | # Top-100 SH with the largest difference betw. total DPI and α-ICON (α=0.999) ├ uk_organisations_top100_holders_diff_transitive_npi_2021_long_2aug21.csv | # Top-100 SH with the largest difference betw. total NPI and α-ICON (α=0.999) └ network_examples/ # Visualisations of selected networks ``` We provide an annotated `Makefile` that documents the data analysis in our papers. To build the ‘α-Indirect Control in Onion-like Networks’ paper run `make alphaicon_paper` when in the repository folder. Please note that those commands will not produce any publication-ready output files (e.g. tables or figures): the export statements are commented out in the code. Our intention is to make the analysis pipeline transparent to the readers with the aid of `make`: ![alphaicon_dependencies](https://user-images.githubusercontent.com/3776887/133301812-87f25078-de5a-4bea-b9b0-0e6addb51b2b.png) ## Licence Creative Commons License
Creative Commons License Attribution 4.0 International (CC BY 4.0). Copyright © the respective contributors, as shown by the `AUTHORS` file. People with Significant Control data is distributed by Companies House under Open Government Licence v3.0. Free Company Data Product is distributed by Companies House under Open Government Licence v3.0. ## Contacts Dmitriy Skougarevskiy, Ph.D. dskougarevskiy@eu.spb.ru