Temporal Link Prediction in Social Networks Based on Agent Behavior Synchrony and a Cognitive Mechanism
CoRR(2024)
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.
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