Network structure, metadata and the prediction of missing nodes and annotations

TL;DR

This paper introduces a Bayesian method to evaluate and utilize metadata for predicting network structure and missing nodes, distinguishing between data quality and algorithm performance.

Contribution

It presents a joint generative model and a nonparametric Bayesian framework to assess metadata usefulness in network analysis, focusing on edge prediction rather than direct community alignment.

Findings

Metadata often predicts network edges effectively despite imperfect alignment.

The method improves missing node prediction using metadata.

Metadata contains meaningful structural information even with imperfect correlation.

Abstract

The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure. Most often, the suitability of the annotations as topological descriptors itself is not assessed, and without this it is not possible to ultimately distinguish between actual shortcomings of the community detection algorithms on one hand, and the incompleteness, inaccuracy or structured nature of the data annotations themselves on the other. In this work we present a principled method to access both aspects simultaneously. We construct a joint generative model for the data and metadata, and a nonparametric Bayesian framework to infer its parameters from annotated datasets. We assess the quality of the metadata not according to its direct alignment with the network communities, but rather in its capacity to predict the placement of edges in the network. We also show how this feature can be used to predict the connections to missing nodes when only the metadata is available, as well as missing metadata. By investigating a wide range of datasets, we show that while there are seldom exact agreements between metadata tokens and the inferred data groups, the metadata is often informative of the network structure nevertheless, and can improve the prediction of missing nodes. This shows that the method uncovers meaningful patterns in both the data and metadata, without requiring or expecting a perfect agreement between the two.