Link predictability classes in large node-attributed networks

Antonov, Andrey; Stavinova, Elizaveta; Evmenova, Elizaveta; Chunaev, Petr

doi:10.1007/s13278-022-00912-w

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…It is noted that for most real-world temporal networks, despite the increased complexity of predictability estimation, the upper bound of combined topological-temporal predictability is higher than that of temporal predictability. Furthermore, there are two studies [91,92] focusing on the realized predictability of network links; specifically, the predictability observed through a selected feature-based link prediction model. The authors evaluate link predictability by assessing the error of the chosen model and divide the links within a small portion of the network into high and low predictability classes based on the error value.…”

Section: Network Link Predictabilitymentioning

confidence: 99%

Enhancing Predictability Assessment: An Overview and Analysis of Predictability Measures for Time Series and Network Links

Bezbochina,

Stavinova,

Kovantsev

et al. 2023

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Driven by the variety of available measures intended to estimate predictability of diverse objects such as time series and network links, this paper presents a comprehensive overview of the existing literature in this domain. Our overview delves into predictability from two distinct perspectives: the intrinsic predictability, which represents a data property independent of the chosen forecasting model and serves as the highest achievable forecasting quality level, and the realized predictability, which represents a chosen quality metric for a specific pair of data and model. The reviewed measures are used to assess predictability across different objects, starting from time series (univariate, multivariate, and categorical) to network links. Through experiments, we establish a noticeable relationship between measures of realized and intrinsic predictability in both generated and real-world time series data (with the correlation coefficient being statistically significant at a 5% significance level). The discovered correlation in this research holds significant value for tasks related to evaluating time series complexity and their potential to be accurately predicted.

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Section: Network Link Predictabilitymentioning

confidence: 99%