# Node metadata can produce predictability transitions in network inference problems

arXiv: https://arxiv.org/abs/2103.14424 · HTML: https://mlsanity.com/paper/2103.14424/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [Community Detection in Networks with Node Attributes](https://mlsanity.com/paper/1401.7267/) (2014)
- [The ground truth about metadata and community detection in networks](https://mlsanity.com/paper/1608.05878/) (2016)
- [Stacking Models for Nearly Optimal Link Prediction in Complex Networks](https://mlsanity.com/paper/1909.07578/) (2019)
- [Structure and inference in annotated networks](https://mlsanity.com/paper/1507.04001/) (2015)
- [Community detection with node attributes in multilayer networks](https://mlsanity.com/paper/2004.09160/) (2020)
- [Network reconstruction and community detection from dynamics](https://mlsanity.com/paper/1903.10833/) (2019)
- [Stochastic Block Models with Multiple Continuous Attributes](https://mlsanity.com/paper/1803.02726/) (2018)
- [Network structure, metadata and the prediction of missing nodes and annotations](https://mlsanity.com/paper/1604.00255/) (2016)
- [Community Detection in Bipartite Networks with Stochastic Blockmodels](https://mlsanity.com/paper/2001.11818/) (2020)
- [Accurate and scalable social recommendation using mixed-membership stochastic block models](https://mlsanity.com/paper/1604.01170/) (2016)
- [Optimal prediction of decisions and model selection in social dilemmas using block models](https://mlsanity.com/paper/2004.12151/) (2020)
- [Tensorial and bipartite block models for link prediction in layered networks and temporal networks](https://mlsanity.com/paper/1803.01616/) (2018)
- [Mixed-Membership of Experts Stochastic Blockmodel](https://mlsanity.com/paper/1404.0221/) (2014)

## Abstract (arXiv)

Network inference is the process of learning the properties of complex networks from data. Besides using information about known links in the network, node attributes and other forms of network metadata can help to solve network inference problems. Indeed, several approaches have been proposed to introduce metadata into probabilistic network models and to use them to make better inferences. However, we know little about the effect of such metadata in the inference process. Here, we investigate this issue. We find that, rather than affecting inference gradually, adding metadata causes abrupt transitions in the inference process and in our ability to make accurate predictions, from a situation in which metadata does not play any role to a situation in which metadata completely dominates the inference process. When network data and metadata are partly correlated, metadata optimally contributes to the inference process at the transition between data-dominated and metadata-dominated regimes.