{"type":"ScholarlyArticle","title":"Adversarial Graph Embedding for Ensemble Clustering","authors":[{"name":"Zhiqiang Tao","slug":"zhiqiang-tao","given":"Zhiqiang","family":"Tao","affiliation":"Department of Electrical and Computer Engineering , Northeastern University , Boston , MA"},{"name":"Hongfu Liu","slug":"hongfu-liu","given":"Hongfu","family":"Liu","affiliation":"Michtom School of Computer Science , Brandeis University , Waltham , MA"},{"name":"Jun Li","slug":"jun-li","given":"Jun","family":"Li","affiliation":"Institute of Medical Engineering and Science , Massachusetts Institute of Technology , Cambridge , MA"},{"name":"Zhaowen Wang","slug":"zhaowen-wang","given":"Zhaowen","family":"Wang","affiliation":"Adobe Research , Adobe Systems Incorporated , San Jose , CA"},{"name":"Yun Fu","slug":"yun-fu","given":"Yun","family":"Fu","affiliation":"Department of Electrical and Computer Engineering , Northeastern University , Boston , MA; Khoury College of Computer and Information Sciences , Northeastern University , Boston , MA"}],"abstract":"Ensemble clustering generally integrates basic partitions into a consensus one through a graph partitioning method, which, however, has two limitations: 1) it neglects to reuse original features; 2) obtaining consensus partition with learnable graph representations is still under-explored. In this paper, we propose a novel Adversarial Graph Auto-Encoders (AGAE) model to incorporate ensemble clustering into a deep graph embedding process. Specifically, graph convolutional network is adopted as probabilistic encoder to jointly integrate the information from feature content and consensus graph, and a simple inner product layer is used as decoder to reconstruct graph with the encoded latent variables (i.e., embedding representations). Moreover, we develop an adversarial regularizer to guide the network training with an adaptive partition-dependent prior. Experiments on eight real-world datasets are presented to show the effectiveness of AGAE over several state-of-the-art deep embedding and ensemble clustering methods.","doi":"10.24963/ijcai.2019/494","url":"https://ijcai.org/proceedings/2019/494","pdf":"https://ijcai.org/proceedings/2019/0494.pdf","markdown":"https://ijcai.org/proceedings/2019/494.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/494","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":494,"pages":{"first":3562,"last":3568},"keywords":["Machine Learning: Ensemble Methods","Machine Learning: Unsupervised Learning","Machine Learning: Adversarial Machine Learning","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Bruna et al., 2013] J. 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