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Node Metadata Can Produce Predictability Crossovers in Network Inference Problems
Physical Review X  (IF15.762),  Pub Date : 2022-01-14, DOI: 10.1103/physrevx.12.011010
Oscar Fajardo-Fontiveros, Roger Guimerà, Marta Sales-Pardo

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 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 a crossover in the inference process and in our ability to make accurate predictions, from a situation in which metadata do not play any role to a situation in which metadata completely dominate the inference process. When network data and metadata are partly correlated, metadata optimally contributes to the inference process at the crossover between data-dominated and metadata-dominated regimes.