Towards Quantifying Sampling Bias in Network Inference

TL;DR

This paper investigates how different network sampling strategies and network structures affect the accuracy of relational inference, revealing that homophily significantly influences inference reliability from small samples.

Contribution

It systematically analyzes the impact of sampling strategies and network homophily on inference accuracy using synthetic networks, highlighting complex effects of network structure.

Findings

In heterophilic networks, small samples suffice for accurate inference regardless of sampling strategy.

In homophilic networks, sampling strategies that work in heterophilic networks perform poorly.

Network structure, especially homophily, critically influences the effectiveness of sampling-based inference.

Abstract

Relational inference leverages relationships between entities and links in a network to infer information about the network from a small sample. This method is often used when global information about the network is not available or difficult to obtain. However, how reliable is inference from a small labelled sample? How should the network be sampled, and what effect does it have on inference error? How does the structure of the network impact the sampling strategy? We address these questions by systematically examining how network sampling strategy and sample size affect accuracy of relational inference in networks. To this end, we generate a family of synthetic networks where nodes have a binary attribute and a tunable level of homophily. As expected, we find that in heterophilic networks, we can obtain good accuracy when only small samples of the network are initially labelled, regardless of the sampling strategy. Surprisingly, this is not the case for homophilic networks, and sampling strategies that work well in heterophilic networks lead to large inference errors. These findings suggest that the impact of network structure on relational classification is more complex than previously thought.