Node metadata can produce predictability transitions in network inference problems
This addresses a fundamental gap in understanding metadata effects in network inference, which is incremental but clarifies critical thresholds for practitioners.
The study investigated how node metadata influences network inference, finding that adding metadata causes abrupt transitions in predictability rather than gradual improvements, with optimal contributions occurring at the transition between data-dominated and metadata-dominated regimes.
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.