# Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm

arXiv: https://arxiv.org/abs/1703.09307 · HTML: https://mlsanity.com/paper/1703.09307/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2017.

## Built on by (most cited first, 16 of 16)

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- [Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes](https://mlsanity.com/paper/2212.02055/) (2022)
- [Layer-diverse Negative Sampling for Graph Neural Networks](https://mlsanity.com/paper/2403.11408/) (2024)
- [A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management](https://mlsanity.com/paper/2306.02806/) (2023)
- [Graph-based Local Climate Classification in Iran](https://mlsanity.com/paper/2110.09209/) (2021)
- [Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks](https://mlsanity.com/paper/2111.15623/) (2021)
- [A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection](https://mlsanity.com/paper/2112.04388/) (2021)
- [Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach](https://mlsanity.com/paper/2307.04668/) (2023)
- [Graph Cuts with Arbitrary Size Constraints Through Optimal Transport](https://mlsanity.com/paper/2402.04732/) (2024)
- [Surrogate models for diffusion on graphs via sparse polynomials](https://mlsanity.com/paper/2502.06595/) (2025)
- [Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning](https://mlsanity.com/paper/2506.10628/) (2025)
- [Edge Augmentation on Disconnected Graphs via Eigenvalue Elevation](https://mlsanity.com/paper/2207.05301/) (2022)
- [UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services](https://mlsanity.com/paper/2306.04144/) (2023)
- [A Community-Enhanced Graph Representation Model for Link Prediction](https://mlsanity.com/paper/2512.21166/) (2025)

## Abstract (arXiv)

We introduce a community detection algorithm (Fluid Communities) based on the idea of fluids interacting in an environment, expanding and contracting as a result of that interaction. Fluid Communities is based on the propagation methodology, which represents the state-of-the-art in terms of computational cost and scalability. While being highly efficient, Fluid Communities is able to find communities in synthetic graphs with an accuracy close to the current best alternatives. Additionally, Fluid Communities is the first propagation-based algorithm capable of identifying a variable number of communities in network. To illustrate the relevance of the algorithm, we evaluate the diversity of the communities found by Fluid Communities, and find them to be significantly different from the ones found by alternative methods.