大数据 (Jul 2024)
A dual channel semi-supervised network representation learning model
Abstract
In semi-supervised network representation learning, node labels play an important role in guiding the establishment of network mapping relationships among different spaces.However, in many practical tasks, the available label information is usually limited or difficult to obtain, which makes it difficult to provide sufficient and effective supervision to the process of learning low-dimensional network representations.In order to solve this problem, a dual channel semisupervised network representation learning model is proposed, which is composed of two information transmission channels, namely self-supervised and semi-supervised channels, with the framework of autoencoder.The self-supervised information and the label information provide guidance for the establishment of network representation mapping in the two channels respectively, and they form such a sense of information complementation and enhancement for the learning process.Considering the possible information redundancy between the two channels, a redundancy recognition and elimination mechanism is designed in the perspective of mutual information.On this basis, an integrated optimization model is constructed to combine the self-supervised learning and the semi-supervised learning into a collaborative mechanism, which enables the learned representations to capture and preserve the structure and characteristics of the network.Experimental results on several real datasets show that the network representations learned by the proposed model can achieve better performance than baseline methods in node classification, clustering and visualization tasks.
