Temporal Link Prediction via Adjusted Sigmoid Function and 2-Simplex Sructure

TL;DR

This paper introduces TLPSS, a novel model for temporal link prediction that combines an adjusted sigmoid function with 2-simplex structures to better utilize historical and high-order network information, improving prediction accuracy.

Contribution

The paper proposes a new temporal link prediction model that integrates an adjusted sigmoid decay and 2-simplex high-order structures to enhance performance in dynamic networks.

Findings

Improves link prediction accuracy by 15% on average.

Effectively models the lifecycle of edges in temporal networks.

Demonstrates superior performance on six real-world datasets.

Abstract

Temporal network link prediction is an important task in the field of network science, and has a wide range of applications in practical scenarios. Revealing the evolutionary mechanism of the network is essential for link prediction, and how to effectively utilize the historical information for temporal links and efficiently extract the high-order patterns of network structure remains a vital challenge. To address these issues, in this paper, we propose a novel temporal link prediction model with adjusted sigmoid function and 2-simplex structure (TLPSS). The adjusted sigmoid decay mode takes the active, decay and stable states of edges into account, which properly fits the life cycle of information. Moreover, the latent matrix sequence is introduced, which is composed of simplex high-order structure, to enhance the performance of link prediction method since it is highly feasible in sparse network. Combining the life cycle of information and simplex high-order structure, the overall performance of TLPSS is achieved by satisfying the consistency of temporal and structural information in dynamic networks. Experimental results on six real-world datasets demonstrate the effectiveness of TLPSS, and our proposed model improves the performance of link prediction by an average of 15% compared to other baseline methods.