Task-driven sampling of attributed networks

TL;DR

This paper presents attribute-aware sampling techniques for attributed networks, improving data mining tasks like clustering and classification by capturing node content more effectively than traditional structure-focused samplers.

Contribution

It introduces new attribute-aware samplers based on Information Theoretic principles, demonstrating their bias towards content and superior performance in real-world and synthetic datasets.

Findings

Attribute-aware samplers outperform traditional methods in clustering.

They effectively capture node content for classification.

Samplers are unbiased in the limit, similar to uniform sampling.

Abstract

This paper introduces new techniques for sampling attributed networks to support standard Data Mining tasks. The problem is important for two reasons. First, it is commonplace to perform data mining tasks such as clustering and classification of network attributes (attributes of the nodes, including social media posts). Furthermore, the extraordinarily large size of real-world networks necessitates that we work with a smaller graph sample. Second, while random sampling will provide an unbiased estimate of content, random access is often unavailable for many networks. Hence, network samplers such as Snowball sampling, Forest Fire, Random Walk, Metropolis-Hastings Random Walk are widely used; however, these attribute-agnostic samplers were designed to capture salient properties of network structure, not node content. The latter is critical for clustering and classification tasks. There are three contributions of this paper. First, we introduce several attribute-aware samplers based on Information Theoretic principles. Second, we prove that these samplers have a bias towards capturing new content, and are equivalent to uniform sampling in the limit. Finally, our experimental results over large real-world datasets and synthetic benchmarks are insightful: attribute-aware samplers outperform both random sampling and baseline attribute-agnostic samplers by a wide margin in clustering and classification tasks.