Evaluating Social Networks Using Task-Focused Network Inference

TL;DR

This paper introduces a framework to evaluate how well social network structures support specific predictive tasks, comparing existing networks with data-inferred alternatives, demonstrated through a music preference classification case study.

Contribution

It proposes a general, interpretable framework for assessing network suitability for predictive tasks, including models and measures for comparison, applied to a real-world social network dataset.

Findings

Existing networks vary in effectiveness for different tasks

Data-inferred networks can outperform original networks in prediction

Framework aids in selecting appropriate network models for specific applications

Abstract

Networks are representations of complex underlying social processes. However, the same given network may be more suitable to model one behavior of individuals than another. In many cases, aggregate population models may be more effective than modeling on the network. We present a general framework for evaluating the suitability of given networks for a set of predictive tasks of interest, compared against alternative, networks inferred from data. We present several interpretable network models and measures for our comparison. We apply this general framework to the case study on collective classification of music preferences in a newly available dataset of the Last.fm social network.