{"type":"ScholarlyArticle","title":"Attributed Graph Clustering via Adaptive Graph Convolution","authors":[{"name":"Xiaotong Zhang","slug":"xiaotong-zhang","given":"Xiaotong","family":"Zhang","affiliation":"Department of Computing , The Hong Kong Polytechnic University"},{"name":"Han Liu","slug":"han-liu","given":"Han","family":"Liu","affiliation":"Department of Computing , The Hong Kong Polytechnic University"},{"name":"Qimai Li","slug":"qimai-li","given":"Qimai","family":"Li","affiliation":"Department of Computing , The Hong Kong Polytechnic University"},{"name":"Xiao-Ming Wu","slug":"xiao-ming-wu","given":"Xiao-Ming","family":"Wu","affiliation":"Department of Computing , The Hong Kong Polytechnic University"}],"abstract":"Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content information, and several recent methods based on it have achieved promising clustering performance on some real attributed networks. However, there is limited understanding of how graph convolution affects clustering performance and how to properly use it to optimize performance for different graphs. Existing methods essentially use graph convolution of a fixed and low order that only takes into account neighbours within a few hops of each node, which underutilizes node relations and ignores the diversity of graphs. In this paper, we propose an adaptive graph convolution method for attributed graph clustering that exploits high-order graph convolution to capture global cluster structure and adaptively selects the appropriate order for different graphs. We establish the validity of our method by theoretical analysis and extensive experiments on benchmark datasets. Empirical results show that our method compares favourably with state-of-the-art methods.","doi":"10.24963/ijcai.2019/601","url":"https://ijcai.org/proceedings/2019/601","pdf":"https://ijcai.org/proceedings/2019/0601.pdf","markdown":"https://ijcai.org/proceedings/2019/601.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/601","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":601,"pages":{"first":4327,"last":4333},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Clustering","Machine Learning: Relational Learning"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal and Reddy, 2014] Charu C Aggarwal and Chan- dan K Reddy. Data Clustering: Algorithms and Applica- tions. CRC Press, Boca Raton, 2014.","title":"Data Clustering: Algorithms and Applications","authors":"C Charu, Chandan K Aggarwal, Reddy","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bojchevski and Günnemann, 2018] Aleksandar Bojchevski and Stephan Günnemann. Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure. In AAAI, 2018.","title":"Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure","authors":"Aleksandar Bojchevski, Stephan Günnemann","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cai et al., 2018] HongYun Cai, Vincent W. Zheng, and Kevin Chen-Chuan Chang. A comprehensive survey of graph embedding: Problems, techniques, and application- s. TKDE, 30(9):1616-1637, 2018.","title":"A comprehensive survey of graph embedding: Problems, techniques, and applications","authors":"Hongyun Cai, Vincent W Zheng, Kevin Chen, -Chuan Chang","year":"2018","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cao et al., 2015] Shaosheng Cao, Wei Lu, and Qiongkai X- u. Grarep: Learning graph representations with global structural information. In CIKM, pages 891-900, 2015.","title":"Grarep: Learning graph representations with global structural information","authors":"Cao","year":"2015","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cao et al., 2016] Shaosheng Cao, Wei Lu, and Qiongkai X- u. Deep neural networks for learning graph representation- s. In AAAI, pages 1145-1152, 2016.","title":"Deep neural networks for learning graph representations","authors":"Cao","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chang and Blei, 2009] Jonathan Chang and David Blei. Re- lational topic models for document networks. In Artificial Intelligence and Statistics, pages 81-88, 2009.","title":"Relational topic models for document networks","authors":"Chang, Blei, Jonathan Chang, David Blei","year":"2009","venue":"Artificial Intelligence and Statistics","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chung and Graham, 1997] Fan RK Chung and Fan Chung Graham. Spectral graph theory. Number 92. American Mathematical Society, 1997.","title":"Spectral graph theory","authors":"Fan Rk, Chung, Fan Chung, Graham","year":"1997","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In KDD, pages 855-864, 2016.","title":"Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks","authors":"Leskovec Grover","year":"2016","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[He et al., 2017] Dongxiao He, Zhiyong Feng, Di Jin, Xi- aobao Wang, and Weixiong Zhang. Joint identification of network communities and semantics via integrative mod- eling of network topologies and node contents. In AAAI, pages 116-124, 2017.","title":"Joint identification of network communities and semantics via integrative modeling of network topologies and node contents","authors":"Dongxiao He, Zhiyong Feng, Di Jin, Xiaobao Wang, Weixiong Zhang","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Variational graph auto-encoders. NIPS Workshop on Bayesian Deep Learning, 2016.","title":"Variational graph auto-encoders","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":"NIPS Workshop on Bayesian Deep Learning","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kipf and Welling, 2017] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In ICLR, 2017.","title":"Semi-supervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2018] Ye Li, Chaofeng Sha, Xin Huang, and Yanchun Zhang. Community detection in attributed graph- s: an embedding approach. In AAAI, pages 338-345, 2018.","title":"Community detection in attributed graphs: an embedding approach","authors":"Ye Li, Chaofeng Sha, Xin Huang, Yanchun Zhang","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2019] Qimai Li, Xiao-Ming Wu, Han Liu, X- iaotong Zhang, and Zhichao Guan. Label efficien- t semi-supervised learning via graph filtering. CoRR, ab- s/1901.09993, 2019.","title":"Label efficient semi-supervised learning via graph filtering","authors":"Qimai Li, Xiao-Ming Wu, Han Liu, Xiaotong Zhang, Zhichao Guan","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Newman, 2006] Mark EJ Newman. Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74(3):036104, 2006.","title":"Finding community structure in networks using the eigenvectors of matrices","authors":"Mark Ej Newman","year":"2006","venue":"Physical Review E","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Nikolentzos et al., 2017] Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis. Matching node embeddings for graph similarity. In AAAI, pages 2429- 2435, 2017.","title":"Matching node embeddings for graph similarity","authors":"Giannis Nikolentzos, Polykarpos Meladianos, Michalis Vazirgiannis","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Pan et al., 2018] Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. Adversarially regu- larized graph autoencoder for graph embedding. In IJCAI, pages 2609-2615, 2018.","title":"Adversarially regularized graph autoencoder for graph embedding","authors":"Pan","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/362"},{"position":17,"raw":"[Perona and Freeman, 1998] Pietro Perona and William Freeman. A factorization approach to grouping. In EC- CV, pages 655-670, 1998.","title":"A factorization approach to grouping","authors":"Freeman ; Perona, Pietro Perona, William Freeman","year":"1998","venue":"EC-CV","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social rep- resentations. In KDD, pages 701-710, 2014.","title":"Deepwalk: Online learning of social representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Schaeffer, 2007] Satu Elisa Schaeffer. Graph clustering. Computer Science Review, 1(1):27-64, 2007.","title":"Graph clustering","authors":"Elisa Satu, Schaeffer","year":"2007","venue":"Computer Science Review","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Shuman et al., 2013] David I Shuman, Sunil K Narang, Pas- cal Frossard, Antonio Ortega, and Pierre Vandergheynst. The emerging field of signal processing on graphs: Ex- tending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Maga- zine, 30(3):83-98, 2013.","title":"The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains","authors":"David I Shuman, K Sunil, Pascal Narang, Antonio Frossard, Pierre Ortega, Vandergheynst","year":"2013","venue":"IEEE Signal Processing Magazine","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Van der Maaten and Hinton, 2008] L.J.P. Van der Maaten and G.E. Hinton. Visualizing high-dimensional data using t-SNE. Journal of Machine Learning Research, 9:2579- 2605, 2008.","title":"Visualizing high-dimensional data using t-SNE","authors":"Van Der Maaten, ; ] L J P Hinton, G E Van Der Maaten, Hinton","year":"2008","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Von Luxburg, 2007] Ulrike Von Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17(4):395- 416, 2007.","title":"A tutorial on spectral clustering","authors":"Ulrike Von, Luxburg","year":"2007","venue":"Statistics and Computing","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2016a] Daixin Wang, Peng Cui, and Wenwu Zhu. Structural deep network embedding. In KDD, pages 1225-1234, 2016.","title":"Structural deep network embedding","authors":"Wang","year":"2016","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2016b] Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang, and Weixiong Zhang. Semantic community identification in large attribute networks. In AAAI, pages 265-271, 2016.","title":"Semantic community identification in large attribute networks","authors":"Wang","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2017] Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang. Mgae: Marginalized graph autoencoder for graph clustering. In CIKM, pages 889- 898, 2017.","title":"Mgae: Marginalized graph autoencoder for graph clustering","authors":"Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Xia et al., 2014] Rongkai Xia, Yan Pan, Lei Du, and Jian Yin. Robust multi-view spectral clustering via low-rank and sparse decomposition. In AAAI, pages 2149-2155, 2014.","title":"Robust multi-view spectral clustering via low-rank and sparse decomposition","authors":"Xia","year":"2014","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Yang et al., 2009] Tianbao Yang, Rong Jin, Yun Chi, and Shenghuo Zhu. Combining link and content for commu- nity detection: a discriminative approach. In KDD, pages 927-936, 2009.","title":"Combining link and content for community detection: a discriminative approach","authors":"Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu","year":"2009","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yang et al., 2013] Jaewon Yang, Julian McAuley, and Jure Leskovec. Community detection in networks with node attributes. In ICDM, pages 1151-1156, 2013.","title":"Community detection in networks with node attributes","authors":"Jaewon Yang, Julian Mcauley, Jure Leskovec","year":"2013","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Yang et al., 2015] Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang. Network represen- tation learning with rich text information. In IJCAI, pages 2111-2117, 2015.","title":"Network representation learning with rich text information","authors":"Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Y Chang","year":"2015","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2015/299"},{"position":30,"raw":"[Ye et al., 2018] Fanghua Ye, Chuan Chen, and Zibin Zheng. Deep autoencoder-like nonnegative matrix factorization for community detection. In CIKM, pages 1393-1402, 2018.","title":"Deep autoencoder-like nonnegative matrix factorization for community detection","authors":"Ye","year":"2018","venue":"CIKM","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}