# Term-community-based topic detection with variable resolution

arXiv: https://arxiv.org/abs/2103.13550 · HTML: https://mlsanity.com/paper/2103.13550/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 80 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [A network approach to topic models](https://mlsanity.com/paper/1708.01677/) (2017)
- [Community detection in networks: Modularity optimization and maximum likelihood are equivalent](https://mlsanity.com/paper/1606.02319/) (2016)
- [A high-reproducibility and high-accuracy method for automated topic classification](https://mlsanity.com/paper/1402.0422/) (2014)
- [From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings](https://mlsanity.com/paper/1707.08098/) (2017)
- [Co-word Maps and Topic Modeling: A Comparison Using Small and Medium-Sized Corpora (n < 1000)](https://mlsanity.com/paper/1511.03020/) (2015)
- [Consistency of community structure in complex networks](https://mlsanity.com/paper/1908.09867/) (2019)
- [Topic segmentation via community detection in complex networks](https://mlsanity.com/paper/1512.01384/) (2015)
- [The Power of Communities: A Text Classification Model with Automated Labeling Process Using Network Community Detection](https://mlsanity.com/paper/1909.11706/) (2019)
- [TeCoMiner: Topic Discovery Through Term Community Detection](https://mlsanity.com/paper/2103.12882/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight](https://mlsanity.com/paper/2410.16397/) (2024)

## Abstract (arXiv)

Network-based procedures for topic detection in huge text collections offer an intuitive alternative to probabilistic topic models. We present in detail a method that is especially designed with the requirements of domain experts in mind. Like similar methods, it employs community detection in term co-occurrence graphs, but it is enhanced by including a resolution parameter that can be used for changing the targeted topic granularity. We also establish a term ranking and use semantic word-embedding for presenting term communities in a way that facilitates their interpretation. We demonstrate the application of our method with a widely used corpus of general news articles and show the results of detailed social-sciences expert evaluations of detected topics at various resolutions. A comparison with topics detected by Latent Dirichlet Allocation is also included. Finally, we discuss factors that influence topic interpretation.