AFS-BERT: Information entropy-based adaptive fusion sampling and Bert embedding model for link prediction

Abstract

Link prediction is an important problem in complex network analysis, which can discover missing or possible future edges in the network. In recent years, link prediction methods based on network representation learning have made progress. But there are two problems with these methods. One is that neighborhood-based node sampling methods cannot handle the situation between two nodes that do not have any common neighbors. The other is the Skip-Gram-based embedding model that represents nodes as static vectors, which cannot reflect the various meanings of nodes. To overcome these two limitations, this paper proposes a method called AFS-BERT (Information entropy based Adaptive Fusion Sampling and BERT embedding model). First, this method defines a centrality score based on adjacency information entropy, which reflects the global and local importance of nodes. Second, we propose a sampling method that adaptively fuses two different strategies using the centrality score. Finally, the BERT-based embedding model is used to realize the low-dimensional dynamic vector representation of nodes. Experimental result on six real-world network datasets shows that AFS-BERT has better performance. Compared with methods of the same type, AFS-BERT achieves upto 6.7% improvement.