Abstract
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.
Highlights
Aiming at the link prediction problem of dynamic networks, this paper proposes a temporal link prediction approach based on community multi-feature fusion and embedded representation, which combines the methods of influence optimization, community detection, and network embedded representation based on the network topology information
Set the temporal attenuation coefficient of collective influence α = 0.9, and set the random walk parameters, including the dimensions = 128, the walk-length = 80, the num-walks = 10, the window-size = 10, and concatenate the node collective influence, the three centrality influence, and the walking embedded vector to predict by using the similarity score of Equation (24), which is recorded as TLP-CCC
This paper proposes three novel similarity indices, including SCI based on collective influence, SCom based on community biased walk, and SCCC based on multi-feature fusion
Summary
Link prediction method has been applied to community detection, anomaly detection, influence analysis, and recommendation systems for complex networks. The method based on likelihood analysis uses the known topology and attribute information to calculate the probability of nonexistent edges. Pan et al [14] combines clustering mechanisms to propose a conditional probability model of the closed paths These methods can make good use of topological structure information, the computational complexity of the algorithms is high and is not applicable for large-scale networks; machine learning methods have been studied in recent years. Aiming at the link prediction problem of dynamic networks, this paper proposes a temporal link prediction approach based on community multi-feature fusion and embedded representation, which combines the methods of influence optimization, community detection, and network embedded representation based on the network topology information. The experimental results on nine real dynamic network data sets show that the proposed method outperforms the traditional classical temporal link prediction methods under AUC evaluation metric
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