MT-MNA: Multiple Network Alignment with Absent Priori Annotation

Haichao Fu, Liangjun Zang, Baojie Tian, Songlin Hu

Abstract: Network alignment serves as a key methodology in data analysis, enabling a understanding of intricate structures within multiple networks. The discovery of potential aligned pairs between networks is a prerequisite for a wide spectrum of applications, e.g. cross-domain recommendation or re-identification attack. Presently, most existing network alignment methods involve only two networks, remaining challenges in scenarios with multiple networks. In addition, acquiring cross-network links often poses a significant challenge, resulting in numerous instances where priori annotation information is absent. Our research confronts these problems by proposing Multi-hop Transformation Multiple Network Alignment with Absent Priori Annotation (MT-MNA). The core idea is to simultaneously align multiple networks and address the scenarios of absent priori annotation by fully utilizing the rich-annotated network pairs. Through rigorous experiments on real-world datasets, our method demonstrates superior precision and adaptability to the challenging condition of absent priori annotation in multiple network alignment, overperforming existing state-of-the-art methods.