# Community Detection for Heterogeneous Multiple Social Networks

arXiv: https://arxiv.org/abs/2405.04371 · HTML: https://mlsanity.com/paper/2405.04371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 7 of 7)

- [Community Detection in Multiplex Networks](https://mlsanity.com/paper/1910.07646/) (2019)
- [Multi-View Multiple Clustering](https://mlsanity.com/paper/1905.05053/) (2019)
- [Spatial-Aware Local Community Detection Guided by Dominance Relation](https://mlsanity.com/paper/2203.15215/) (2022)
- [Mutual Clustering on Comparative Texts via Heterogeneous Information Networks](https://mlsanity.com/paper/1903.03762/) (2019)
- [Finding compact communities in large graphs](https://mlsanity.com/paper/1410.2105/) (2014)
- [Community Detection Across Multiple Social Networks based on Overlapping Users](https://mlsanity.com/paper/1909.09007/) (2019)
- [Understanding User Topic Preferences across Multiple Social Networks](https://mlsanity.com/paper/2103.07654/) (2021)

## Abstract (arXiv)

The community plays a crucial role in understanding user behavior and network characteristics in social networks. Some users can use multiple social networks at once for a variety of objectives. These users are called overlapping users who bridge different social networks. Detecting communities across multiple social networks is vital for interaction mining, information diffusion, and behavior migration analysis among networks. This paper presents a community detection method based on nonnegative matrix tri-factorization for multiple heterogeneous social networks, which formulates a common consensus matrix to represent the global fused community. Specifically, the proposed method involves creating adjacency matrices based on network structure and content similarity, followed by alignment matrices which distinguish overlapping users in different social networks. With the generated alignment matrices, the method could enhance the fusion degree of the global community by detecting overlapping user communities across networks. The effectiveness of the proposed method is evaluated with new metrics on Twitter, Instagram, and Tumblr datasets. The results of the experiments demonstrate its superior performance in terms of community quality and community fusion.