Unsupervised Multiple Network Alignment with Multinominal GAN and Variational Inference

Yang Zhou, Jiaxiang Ren, Ruoming Jin, Zijie Zhang, Dejing Dou, Da Yan

Abstract: Network alignment techniques, which aim to identify the same entities across multiple networks, often suffer challenges from feature inconsistency to transitivity law preservation. This paper presents a purely unsupervised network alignment method, KEMINA, with three original contributions. First, in order to address the feature inconsistency issue, an adversarial kernel embedding technique is proposed to extract network-invariant information among multiple networks without prior alignment knowledge, and project them into the common embedding space. Second, a multinomial generative adversarial network (GAN) model is developed to train multiple network alignment tasks simultaneously in an unsupervised manner with preserving the transitivity law property. Third but last, a variational inference model is designed to alleviate the data sparsity and inadequate training issues by filling realistic detail for vertices with sparse features and generating real-looking supplementary vertex samples within limited training opportunity of each pair of source and target networks.