Cross-domain entity identity association analysis and prediction based on representation learning

Abstract

Cross-domain identity association of network entities is a significant research challenge and a vital issue of practical value in relationship discovery and service recommendation between things in the Internet of things, cyberspace resources surveying mapping, threat tracking, and intelligent recommendation. This task usually adds additional difficulty to the research in practical applications due to the need to link across multiple platforms. The existing entity identity association methods in cross-domain networks mainly use the attribute information, generated content, and network structure information of network user entities but do not fully use the inherent strong positioning characteristics of active nodes in the network. In this article, we analyzed the structural characteristics of existing relational networks. We found that the hub node has the role of identity association positioning, and the importance of identity association reflected by different nodes is different. Moreover, we creatively designed a network representation learning method. We proposed a supervised learning identity association model combined with a representation learning method. Experiments on the public data set show that using the identity association method proposed in this article, the ranking accuracy of user entity association similarity is about 30% and 25% higher than the existing two typical methods.