An Interlayer Link Prediction Method Based on Edge-Weighted Embedding

Abstract

Presently, users usually register accounts on online social networks (OSNs). Identifying the same user in different networks is also known as interlayer link prediction. Most existing interlayer link prediction studies use embedding methods, which represent nodes in a common representation space by learning mapping functions. However, these studies often directly model links within the pre-embedding layer as equal weights, fail to effectively distinguish the strength of edge relationships, and do not fully utilize network topology information. In this paper, we propose an interlayer link prediction model based on weighted embedding of connected edges within the network layer, which models the links within the network layer as weighted graphs to better represent the network and then uses appropriate embedding methods to represent the network in a low-dimensional space. After embedding, vector similarity and distance similarity are used as comprehensive evaluation scores. This paper has conducted a large number of simulation experiments on actual networks. The results show that our proposed model has higher prediction accuracy in all aspects than current advanced models and can achieve the highest accuracy when the training frequency is low, which proves the validity of the proposed model.