# LAGOON: An Analysis Tool for Open Source Communities

arXiv: https://arxiv.org/abs/2201.11657 · HTML: https://mlsanity.com/paper/2201.11657/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [Learning Role-based Graph Embeddings](https://mlsanity.com/paper/1802.02896/) (2018)
- [Temporal Graph Kernels for Classifying Dissemination Processes](https://mlsanity.com/paper/1911.05496/) (2019)
- [A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research](https://mlsanity.com/paper/1802.09416/) (2018)
- [A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits](https://mlsanity.com/paper/2003.08349/) (2020)
- [Understanding the group dynamics and success of teams](https://mlsanity.com/paper/1407.2893/) (2014)
- [Which contributions count? Analysis of attribution in open source](https://mlsanity.com/paper/2103.11007/) (2021)

## Abstract (arXiv)

This paper presents LAGOON -- an open source platform for understanding the complex ecosystems of Open Source Software (OSS) communities. The platform currently utilizes spatiotemporal graphs to store and investigate the artifacts produced by these communities, and help analysts identify bad actors who might compromise an OSS project's security. LAGOON provides ingest of artifacts from several common sources, including source code repositories, issue trackers, mailing lists and scraping content from project websites. Ingestion utilizes a modular architecture, which supports incremental updates from data sources and provides a generic identity fusion process that can recognize the same community members across disparate accounts. A user interface is provided for visualization and exploration of an OSS project's complete sociotechnical graph. Scripts are provided for applying machine learning to identify patterns within the data. While current focus is on the identification of bad actors in the Python community, the platform's reusability makes it easily extensible with new data and analyses, paving the way for LAGOON to become a comprehensive means of assessing various OSS-based projects and their communities.