# HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories

arXiv: https://arxiv.org/abs/1910.07115 · HTML: https://mlsanity.com/paper/1910.07115/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 19 of 19)

- [MATCH: Metadata-Aware Text Classification in A Large Hierarchy](https://mlsanity.com/paper/2102.07349/) (2021)
- [Code Recommendation for Open Source Software Developers](https://mlsanity.com/paper/2210.08332/) (2022)
- [Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts](https://mlsanity.com/paper/2212.06002/) (2022)
- [Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification](https://mlsanity.com/paper/2202.05932/) (2022)
- [MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information](https://mlsanity.com/paper/2111.04022/) (2021)
- [Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers](https://mlsanity.com/paper/2306.14003/) (2023)
- [GitRanking: A Ranking of GitHub Topics for Software Classification using Active Sampling](https://mlsanity.com/paper/2205.09379/) (2022)
- [Repo2Vec: A Comprehensive Embedding Approach for Determining Repository Similarity](https://mlsanity.com/paper/2107.05112/) (2021)
- [Semantically-enhanced Topic Recommendation System for Software Projects](https://mlsanity.com/paper/2206.00085/) (2022)
- [Automatically Categorising GitHub Repositories by Application Domain](https://mlsanity.com/paper/2208.00269/) (2022)
- [Entity Set Co-Expansion in StackOverflow](https://mlsanity.com/paper/2212.02271/) (2022)
- [Hierarchy-Aware T5 with Path-Adaptive Mask Mechanism for Hierarchical Text Classification](https://mlsanity.com/paper/2109.08585/) (2021)
- [Antipatterns in Software Classification Taxonomies](https://mlsanity.com/paper/2204.08880/) (2022)
- [Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains](https://mlsanity.com/paper/2401.13129/) (2024)
- [DRAGON: Robust Classification for Very Large Collections of Software Repositories](https://mlsanity.com/paper/2602.09071/) (2026)
- [LabelGit: A Dataset for Software Repositories Classification using Attributed Dependency Graphs](https://mlsanity.com/paper/2103.08890/) (2021)
- [Topical: Learning Repository Embeddings from Source Code using Attention](https://mlsanity.com/paper/2208.09495/) (2022)
- [Fine-Tuning Topics through Weighting Aspect Keywords](https://mlsanity.com/paper/2502.08496/) (2025)
- [ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems](https://mlsanity.com/paper/2606.21597/) (2026)

## Abstract (arXiv)

GitHub has become an important platform for code sharing and scientific exchange. With the massive number of repositories available, there is a pressing need for topic-based search. Even though the topic label functionality has been introduced, the majority of GitHub repositories do not have any labels, impeding the utility of search and topic-based analysis. This work targets the automatic repository classification problem as keyword-driven hierarchical classification. Specifically, users only need to provide a label hierarchy with keywords to supply as supervision. This setting is flexible, adaptive to the users' needs, accounts for the different granularity of topic labels and requires minimal human effort. We identify three key challenges of this problem, namely (1) the presence of multi-modal signals; (2) supervision scarcity and bias; (3) supervision format mismatch. In recognition of these challenges, we propose the HiGitClass framework, comprising of three modules: heterogeneous information network embedding; keyword enrichment; topic modeling and pseudo document generation. Experimental results on two GitHub repository collections confirm that HiGitClass is superior to existing weakly-supervised and dataless hierarchical classification methods, especially in its ability to integrate both structured and unstructured data for repository classification.