An Online and Nonuniform Timeslicing Method for Network Visualisation

TL;DR

This paper introduces an online, nonuniform timeslicing technique for visualizing streaming temporal networks, reducing clutter and enhancing pattern recognition for faster decision-making.

Contribution

The paper proposes a novel online, nonuniform timeslicing method that adapts to network structure and event bursts, improving visualization clarity in streaming networks.

Findings

Effectively reduces visual clutter during event bursts.

Automatically selects optimal timeslices based on network activity.

Enhances speed and reliability of global pattern detection.

Abstract

Visual analysis of temporal networks comprises an effective way to understand the network dynamics, facilitating the identification of patterns, anomalies, and other network properties, thus resulting in fast decision making. The amount of data in real-world networks, however, may result in a layout with high visual clutter due to edge overlapping. This is particularly relevant in the so-called streaming networks, in which edges are continuously arriving (online) and in non-stationary distribution. All three network dimensions, namely node, edge, and time, can be manipulated to reduce such clutter and improve readability. This paper presents an online and nonuniform timeslicing method, thus considering the underlying network structure and addressing streaming network analyses. We conducted experiments using two real-world networks to compare our method against uniform and nonuniform timeslicing strategies. The results show that our method automatically selects timeslices that effectively reduce visual clutter in periods with bursts of events. As a consequence, decision making based on the identification of global temporal patterns becomes faster and more reliable.