Finding Proper Time Intervals for Dynamic Network Extraction
arXiv:2206.12678 · doi:10.1088/1742-5468/abed45
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.
arXiv comment19 pages, 12 figures
References in corpus (13)
- The structure and function of complex networks
- Temporal Networks
- The Dynamics of Viral Marketing
- Metrics for Graph Comparison: A Practitioner's Guide
- Circadian pattern and burstiness in mobile phone communication
- Daily rhythms in mobile telephone communication
- Persistence and periodicity in a dynamic proximity network
- Effects of time window size and placement on the structure of aggregated networks
- Detection of timescales in evolving complex systems
- Towards a temporal network analysis of interactive WiFi users
- Sampling of Temporal Networks: Methods and Biases
- A supervised approach to time scale detection in dynamic networks
- Optimal timescale for community detection in growing networks