{"type":"ScholarlyArticle","title":"Multiplex Graph Representation Learning via Bi-level Optimization","authors":[{"name":"Yudi Huang","slug":"yudi-huang","given":"Yudi","family":"Huang","affiliation":"School of Computer Science and Engineering , Guangxi Normal University , Guilin 541004 , China; Guangxi Key Lab of Multisource Information Mining Security , Guilin 541004 , China"},{"name":"Yujie Mo","slug":"yujie-mo","given":"Yujie","family":"Mo","affiliation":"School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu 611731 , China"},{"name":"Yujing Liu","slug":"yujing-liu","given":"Yujing","family":"Liu","affiliation":"School of Computer Science and Engineering , Guangxi Normal University , Guilin 541004 , China; Guangxi Key Lab of Multisource Information Mining Security , Guilin 541004 , China"},{"name":"Ci Nie","slug":"ci-nie","given":"Ci","family":"Nie","affiliation":"School of Computer Science and Engineering , Guangxi Normal University , Guilin 541004 , China; Guangxi Key Lab of Multisource Information Mining Security , Guilin 541004 , China"},{"name":"Guoqiu Wen","slug":"guoqiu-wen","given":"Guoqiu","family":"Wen","affiliation":"School of Computer Science and Engineering , Guangxi Normal University , Guilin 541004 , China; Guangxi Key Lab of Multisource Information Mining Security , Guilin 541004 , China"},{"name":"Xiaofeng Zhu","slug":"xiaofeng-zhu","given":"Xiaofeng","family":"Zhu","affiliation":"School of Computer Science and Engineering , Guangxi Normal University , Guilin 541004 , China; Guangxi Key Lab of Multisource Information Mining Security , Guilin 541004 , China"}],"abstract":"Many multiplex graph representation learning (MGRL) methods have been demonstrated to 1) ignore the globally positive and negative relationships among node features; and 2) usually utilize the node classification task to train both graph structure learning and representation learning parameters, and thus resulting in the problem of edge starvation.\r\nTo address these issues, in this paper, we propose a new MGRL method based on the bi-level optimization. Specifically, in the inner level, we optimize the self-expression matrix to capture the globally positive and negative relationships among nodes, as well as complement them with the local relationships in graph structures. In the outer level, we optimize the parameters of the graph convolutional layer to obtain discriminative node representations. As a result, the graph structure optimization does not depend on the node classification task, which solves the edge starvation problem. Extensive experiments show that our model achieves the superior performance on node classification tasks on all datasets.","doi":"10.24963/ijcai.2024/230","url":"https://ijcai.org/proceedings/2024/230","pdf":"https://ijcai.org/proceedings/2024/0230.pdf","markdown":"https://ijcai.org/proceedings/2024/230.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/230","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":230,"pages":{"first":2081,"last":2089},"keywords":["Data Mining: DM: Mining heterogenous data","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Amari, 1993] Shun-ichi Amari. 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