# Redefining Event Types and Group Evolution in Temporal Data

arXiv: https://arxiv.org/abs/2403.06771 · HTML: https://mlsanity.com/paper/2403.06771/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys](https://mlsanity.com/paper/1506.03645/) (2015)
- [Data Stream Clustering: A Review](https://mlsanity.com/paper/2007.10781/) (2020)
- [Flow stability for dynamic community detection](https://mlsanity.com/paper/2101.06131/) (2021)
- [Measuring Diversity in Heterogeneous Information Networks](https://mlsanity.com/paper/2001.01296/) (2020)
- [Evaluating Community Detection Algorithms for Progressively Evolving Graphs](https://mlsanity.com/paper/2007.08635/) (2020)
- [Predicting Community Evolution in Social Networks](https://mlsanity.com/paper/1505.01709/) (2015)
- [Data compression to choose a proper dynamic network representation](https://mlsanity.com/paper/2010.06885/) (2020)
- [Should I Stay or Should I Go: Predicting Changes in Cluster Membership](https://mlsanity.com/paper/2107.07362/) (2021)

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

- [Characterizing User Archetypes and Discussions on Scored.co](https://mlsanity.com/paper/2407.21753/) (2024)

## Abstract (arXiv)

Groups -- such as clusters of points or communities of nodes -- are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of ``events". However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as ``archetypes" characterized by a unique combination of quantitative dimensions that we call ``facets". Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.