Temporal Link Prediction in Social Networks Based on Agent Behavior Synchrony and a Cognitive Mechanism

TL;DR

This paper introduces a cognitive-inspired model for temporal link prediction in social networks, capturing interaction dynamics and memory effects to improve prediction accuracy across diverse datasets.

Contribution

It proposes a novel cognitive mechanism-based approach that models edge weight changes over time, enhancing heterogeneity capture in temporal link prediction.

Findings

Improves average precision by 9% over baselines.

Different contributions of local structure and synchrony vary across datasets.

Increasing time intervals reduces noise impact and improves predictions.

Abstract

Temporality, a crucial characteristic in the formation of social relationships, was used to quantify the long-term time effects of networks for link prediction models, ignoring the heterogeneity of time effects on different time scales. In this work, we propose a novel approach to link prediction in temporal networks, extending existing methods with a cognitive mechanism that captures the dynamics of the interactions. Our approach computes the weight of the edges and their change over time, similar to memory traces in the human brain, by simulating the process of forgetting and strengthening connections depending on the intensity of interactions. We utilized five ground-truth datasets, which were used to predict social ties, missing events, and potential links. We found: (a) the cognitive mechanism enables more accurate capture of the heterogeneity of the temporal effect, leading to an average precision improvement of 9\% compared to baselines with competitive AUC. (b) the local structure and synchronous agent behavior contribute differently to different types of datasets. (c) appropriately increasing the time intervals, which may reduce the negative impact from noise when dividing time windows to calculate the behavioral synchrony of agents, is effective for link prediction tasks.