TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are g...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2022-02, Vol.24 (2), p.296
Main Authors: Zhu, Yuhang, Liu, Shuxin, Li, Yingle, Li, Haitao
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Attenuation
collective influence
community detection
Expected values
Graph theory
Machine learning
multi feature fusion
Network analysis
Neural networks
Nodes
Optimization
Random walk
representation learning
Social networks
temporal link prediction
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Summary:In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.
ISSN:1099-4300
1099-4300
DOI:10.3390/e24020296