# Should I Stay or Should I Go: Predicting Changes in Cluster Membership

arXiv: https://arxiv.org/abs/2107.07362 · HTML: https://mlsanity.com/paper/2107.07362/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2021.

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

- [Redefining Event Types and Group Evolution in Temporal Data](https://mlsanity.com/paper/2403.06771/) (2024)

## Abstract (arXiv)

Most research on predicting community evolution focuses on changes in the states of communities. Instead, we focus on individual nodes and define the novel problem of predicting whether a specific node stays in the same cluster, moves to another cluster or drops out of the network. We explore variations of the problem and propose appropriate classification features based on local and global node measures. Motivated by the prevalence of machine learning approaches based on embeddings, we also introduce efficiently computed distance-based features using appropriate node embeddings. In addition, we consider chains of features to capture the history of the nodes. Our experimental results depict the complexity of the different formulations of the problem and the suitability of the selected features and chain lengths.