Network Sampling: From Static to Streaming Graphs

TL;DR

This paper introduces a unified framework for network sampling that spans static and streaming graphs, proposing new methods that better preserve graph properties and adapt traditional algorithms for dynamic networks.

Contribution

It presents a spectrum of computational models for network sampling, including a family of graph induction-based methods applicable to both static and streaming graphs, enhancing property preservation.

Findings

Proposed sampling methods outperform traditional algorithms in property preservation.

Modified static algorithms effectively adapt to streaming graph scenarios.

Sampling impacts on relational classification accuracy are analyzed.

Abstract

Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study. For these reasons, a more thorough and complete understanding of network sampling is critical to support the field of network science. In this paper, we outline a framework for the general problem of network sampling, by highlighting the different objectives, population and units of interest, and classes of network sampling methods. In addition, we propose a spectrum of computational models for network sampling methods, ranging from the traditionally studied model based on the assumption of a static domain to a more challenging model that is appropriate for streaming domains. We design a family of sampling methods based on the concept of graph induction that generalize across the full spectrum of computational models (from static to streaming) while efficiently preserving many of the topological properties of the input graphs. Furthermore, we demonstrate how traditional static sampling algorithms can be modified for graph streams for each of the three main classes of sampling methods: node, edge, and topology-based sampling. Our experimental results indicate that our proposed family of sampling methods more accurately preserves the underlying properties of the graph for both static and streaming graphs. Finally, we study the impact of network sampling algorithms on the parameter estimation and performance evaluation of relational classification algorithms.