# Incremental Community Detection in Distributed Dynamic Graph

arXiv: https://arxiv.org/abs/2110.06311 · HTML: https://mlsanity.com/paper/2110.06311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [A Real-Time Detecting Algorithm for Tracking Community Structure of Dynamic Networks](https://mlsanity.com/paper/1407.2683/) (2014)
- [Distributed Community Detection in Dynamic Graphs](https://mlsanity.com/paper/1302.5607/) (2013)
- [Distributed Community Detection with the WCC Metric](https://mlsanity.com/paper/1411.0557/) (2014)
- [Big Graph Mining: Frameworks and Techniques](https://mlsanity.com/paper/1602.03072/) (2016)
- [BLADYG: A Graph Processing Framework for Large Dynamic Graphs](https://mlsanity.com/paper/1701.00546/) (2017)
- [Fast Detection of Community Structures using Graph Traversal in Social Networks](https://mlsanity.com/paper/1707.04459/) (2017)
- [On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs](https://mlsanity.com/paper/1801.05946/) (2018)

## Abstract (arXiv)

Community detection is an important research topic in graph analytics that has a wide range of applications. A variety of static community detection algorithms and quality metrics were developed in the past few years. However, most real-world graphs are not static and often change over time. In the case of streaming data, communities in the associated graph need to be updated either continuously or whenever new data streams are added to the graph, which poses a much greater challenge in devising good community detection algorithms for maintaining dynamic graphs over streaming data. In this paper, we propose an incremental community detection algorithm for maintaining a dynamic graph over streaming data. The contributions of this study include (a) the implementation of a Distributed Weighted Community Clustering (DWCC) algorithm, (b) the design and implementation of a novel Incremental Distributed Weighted Community Clustering (IDWCC) algorithm, and (c) an experimental study to compare the performance of our IDWCC algorithm with the DWCC algorithm. We validate the functionality and efficiency of our framework in processing streaming data and performing large in-memory distributed dynamic graph analytics. The results demonstrate that our IDWCC algorithm performs up to three times faster than the DWCC algorithm for a similar accuracy.