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

by   Ruizhi Zhang, et al.

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 to other baseline methods.


Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN

Network embedding aims to learn low-dimensional representations of nodes...

GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks

In this paper, we generally formulate the dynamics prediction problem of...

Link Prediction for Temporally Consistent Networks

Dynamic networks have intrinsic structural, computational, and multidisc...

Structural Regularity Exploring and Controlling: A Network Reconstruction Perspective

The ubiquitous complex networks are often composed of regular and irregu...

Generative Temporal Link Prediction via Self-tokenized Sequence Modeling

We formalize networks with evolving structures as temporal networks and ...

Enhance Ambiguous Community Structure via Multi-strategy Community Related Link Prediction Method with Evolutionary Process

Most real-world networks suffer from incompleteness or incorrectness, wh...

Scalable Deep Generative Relational Models with High-Order Node Dependence

We propose a probabilistic framework for modelling and exploring the lat...

Please sign up or login with your details

Forgot password? Click here to reset