Finding Proper Time Intervals for Dynamic Network Extraction

TL;DR

This paper introduces a method to identify critical time intervals in dynamic networks by using similarity metrics and statistical thresholds, improving the extraction of meaningful network snapshots for system modeling.

Contribution

It develops three novel similarity metrics and a statistical approach to detect significant system changes without user-defined thresholds.

Findings

Proposed similarities align with network properties and are less noisy.

Similarity scores outperform adjacency correlation in signal quality.

Statistical thresholds enable detection of non-redundant, meaningful network snapshots.

Abstract

Extracting a proper dynamic network for modelling a time-dependent complex system is an important issue. Building a correct model is related to finding out critical time points where a system exhibits considerable change. In this work, we propose to measure network similarity to detect proper time intervals. We develop three similarity metrics, node, link, and neighborhood similarities, for any consecutive snapshots of a dynamic network. Rather than a label or a user-defined threshold, we use statistically expected values of proposed similarities under a null-model to state whether the system changes critically. We experimented on two different data sets with different temporal dynamics: The Wi-Fi access points logs of a university campus and Enron emails. Results show that, first, proposed similarities reflect similar signal trends with network topological properties with less noisy signals, and their scores are scale invariant. Second, proposed similarities generate better signals than adjacency correlation with optimal noise and diversity. Third, using statistically expected values allows us to find different time intervals for a system, leading to the extraction of non-redundant snapshots for dynamic network modelling.