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

• 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
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24020296

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.