{"type":"ScholarlyArticle","title":"Coupled Point Process-based Sequence Modeling for Privacy-preserving Network Alignment","authors":[{"name":"Dixin Luo","slug":"dixin-luo","given":"Dixin","family":"Luo","affiliation":"School of Computer Science and Technology , Beijing Institue of Technology"},{"name":"Haoran Cheng","slug":"haoran-cheng","given":"Haoran","family":"Cheng","affiliation":"School of Computer Science and Technology , Beijing Institue of Technology"},{"name":"Qingbin Li","slug":"qingbin-li","given":"Qingbin","family":"Li","affiliation":"School of Computer Science and Technology , Beijing Institue of Technology"},{"name":"Hongteng Xu","slug":"hongteng-xu","given":"Hongteng","family":"Xu","affiliation":"Gaoling School of Artificial Intelligence , Renmin University of China; Beijing Key Laboratory of Big Data Management and Analysis Methods"}],"abstract":"Network alignment aims at finding the correspondence of nodes across different networks, which is significant for many applications, e.g., fraud detection and crime network tracing across platforms. \r\nIn practice, however, accessing the topological information of different networks is often restricted and even forbidden, considering privacy and security issues. \r\nInstead, what we observed might be the event sequences of the networks' nodes in the continuous-time domain. \r\nIn this study, we develop a coupled neural point process-based (CPP) sequence modeling strategy, which provides a solution to privacy-preserving network alignment based on the event sequences. \r\nOur CPP consists of a coupled node embedding layer and a neural point process module. \r\nThe coupled node embedding layer embeds one network's nodes and explicitly models the alignment matrix between the two networks.\r\nAccordingly, it parameterizes the node embeddings of the other network by the push-forward operation. \r\nGiven the node embeddings, the neural point process module jointly captures the dynamics of the two networks' event sequences.\r\nWe learn the CPP model in a maximum likelihood estimation framework with an inverse optimal transport (IOT) regularizer. \r\nExperiments show that our CPP is compatible with various point process backbones and is robust to the model misspecification issue, which achieves encouraging performance on network alignment. \r\nThe code is available at https://github.com/Dixin-s-Lab/CNPP.","doi":"10.24963/ijcai.2023/678","url":"https://ijcai.org/proceedings/2023/678","pdf":"https://ijcai.org/proceedings/2023/0678.pdf","markdown":"https://ijcai.org/proceedings/2023/678.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/678","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":678,"pages":{"first":6112,"last":6120},"keywords":["AI for Good: Multidisciplinary Topics and Applications","AI for Good: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Adams and Zemel, 2011] Ryan Prescott Adams and Richard S Zemel. 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