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Temporal network link prediction is an important task in the field of networkscience, and has a wide range of applications in practical scenarios. Revealingthe evolutionary mechanism of the network is essential for link prediction, andhow to effectively utilize the historical information for temporal links andefficiently extract the high-order patterns of network structure remains avital challenge. To address these issues, in this paper, we propose a noveltemporal link prediction model with adjusted sigmoid function and 2-simplexstructure (TLPSS). The adjusted sigmoid decay mode takes the active, decay andstable states of edges into account, which properly fits the life cycle ofinformation. Moreover, the latent matrix sequence is introduced, which iscomposed of simplex high-order structure, to enhance the performance of linkprediction method since it is highly feasible in sparse network. Combining thelife cycle of information and simplex high-order structure, the overallperformance of TLPSS is achieved by satisfying the consistency of temporal andstructural information in dynamic networks. Experimental results on sixreal-world datasets demonstrate the effectiveness of TLPSS, and our proposedmodel improves the performance of link prediction by an average of 15% comparedto other baseline methods.

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