Efficient Sampling of Temporal Networks with Preserved Causality Structure

arXiv (Cornell University)

Abstract

In this paper, we extend the classical Color Refinement algorithm for static networks to temporal (undirected and directed) networks. This enables us to design an algorithm to sample synthetic networks that preserves the $d$-hop neighborhood structure of a given temporal network. The higher $d$ is chosen, the better the temporal neighborhood structure of the original network is preserved. Specifically, we provide efficient algorithms that preserve time-respecting ("causal") paths in the networks up to length $d$, and scale to real-world network sizes. We validate our approach theoretically (for Degree and Katz centrality) and experimentally (for edge persistence, causal triangles, and burstiness). An experimental comparison shows that our method retains these key temporal characteristics more effectively than existing randomization methods.

Authors 3

1. Felix I. Stamm Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. Mehdi Naima

   Centre National de la Recherche Scientifique · Sorbonne Université · LIP6

   Affiliation as printed

   Sorbonne Université , CNRS , LIP6 , F-75005 Paris , France

3. Michael T. Schaub Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University