Temporal Network Sampling

Understand

Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real-world.

• However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and predictive modeling tasks.
• In this work, we propose a general framework for temporal network sampling with unbiased estimation.
• We develop online, single-pass sampling algorithms and unbiased estimators for temporal network sampling.