Temporal network compression via network hashing

TL;DR

This paper introduces a matrix algorithm and a hashing framework to efficiently analyze and compress large temporal networks, facilitating the estimation of reachability and out-components while respecting privacy.

Contribution

It presents a novel matrix-based method for out-component identification and a hashing approach to coarsen large temporal networks for easier analysis.

Findings

The matrix algorithm outperforms existing methods in efficiency.

The hashing framework effectively coarsens networks without significant loss of information.

The approach has privacy-preserving implications for network analysis.

Abstract

Pairwise temporal interactions between entities can be represented as temporal networks, which code the propagation of processes such as epidemic spreading or information cascades, evolving on top of them. The largest outcome of these processes is directly linked to the structure of the underlying network. Indeed, a node of a network at given time cannot affect more nodes in the future than it can reach via time-respecting paths. This set of nodes reachable from a source defines an out-component, which identification is costly. In this paper, we propose an efficient matrix algorithm to tackle this issue and show that it outperforms other state-of-the-art methods. Secondly, we propose a hashing framework to coarsen large temporal networks into smaller proxies on which out-components are easier to estimate, and then recombined to obtain the initial components. Our graph hashing solution has implications in privacy respecting representation of temporal networks.