Temporal effects in trend prediction: identifying the most popular nodes in the future

TL;DR

This paper introduces a temporal-based predictor (TBP) for trend prediction in complex networks, leveraging detailed network evolution data to improve accuracy in identifying future popular nodes, including those with low past popularity.

Contribution

The paper proposes a novel temporal-based predictor (TBP) that incorporates exponential aging to enhance trend prediction accuracy in complex networks.

Findings

TBP achieves high accuracy in predicting future popular nodes.

TBP can identify potential high-popularity nodes with low past popularity.

The decay speed in exponential aging significantly influences prediction results.

Abstract

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes' recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail.