# Span-core Decomposition for Temporal Networks: Algorithms and Applications

arXiv: https://arxiv.org/abs/1910.03645 · HTML: https://mlsanity.com/paper/1910.03645/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2019.

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

- [Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity](https://mlsanity.com/paper/2510.03899/) (2025)

## Abstract (arXiv)

When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). In this paper we tackle this task by introducing a notion of temporal core decomposition where each core is associated with two quantities, its coreness, which quantifies how densely it is connected, and its span, which is a temporal interval: we call such cores span-cores. For a temporal network defined on a discrete temporal domain T, the total number of time intervals included in T is quadratic in |T|, so that the total number of span-cores is potentially quadratic in |T| as well. Our first main contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores, i.e., span-cores that are not dominated by any other span-core by both their coreness property and their span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly extract the maximal ones without computing all span-cores. Finally, as a third contribution, we introduce the problem of temporal community search, where a set of query vertices is given as input, and the goal is to find a set of densely-connected subgraphs containing the query vertices and covering the whole underlying temporal domain T. We derive a connection between this problem and the problem of finding (maximal) span-cores. Based on this connection, we show how temporal community search can be solved in polynomial-time via dynamic programming, and how the maximal span-cores can be profitably exploited to significantly speed-up the basic algorithm.