Temporal Link Prediction via Adjusted Sigmoid Function and 2-Simplex
Sructure
- URL: http://arxiv.org/abs/2206.09529v1
- Date: Mon, 20 Jun 2022 01:32:02 GMT
- Title: Temporal Link Prediction via Adjusted Sigmoid Function and 2-Simplex
Sructure
- Authors: Ruizhi Zhang, Qiaozi Wang, Qiming Yang and Wei Wei
- Abstract summary: We propose a novel temporal link prediction model with adjusted sigmoid function and 2-simplex structure (TLPSS)
Our proposed model improves the performance of link prediction by an average of 15% compared to other baseline methods.
- Score: 7.478752512210058
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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