Fast filtering and animation of large dynamic networks

TL;DR

This paper introduces a fast, scalable algorithm for filtering and visualizing large dynamic networks, capturing persistent trends and smoothing transitions efficiently in evolving weighted graphs.

Contribution

The paper presents a novel approximation algorithm for dynamic network filtering that is faster and more scalable than traditional sliding window methods.

Findings

Captures persistent structural trends in dynamic networks

Smoothens transitions between network snapshots

Uses limited memory and processing time

Abstract

Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: i) captures persistent trends in the structure of dynamic weighted networks, ii) smoothens transitions between the snapshots of dynamic network, and iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.