{"type":"ScholarlyArticle","title":"MEGAN: A Generative Adversarial Network for Multi-View Network Embedding","authors":[{"name":"Yiwei Sun","slug":"yiwei-sun","given":"Yiwei","family":"Sun","affiliation":"Department of Computer Science and Engineering , The Pennsylvania State University , USA"},{"name":"Suhang Wang","slug":"suhang-wang","given":"Suhang","family":"Wang","affiliation":"College of Information Sciences and Technology , The Pennsylvania State University , USA"},{"name":"Tsung-Yu Hsieh","slug":"tsung-yu-hsieh","given":"Tsung-Yu","family":"Hsieh","affiliation":"Department of Computer Science and Engineering , The Pennsylvania State University , USA"},{"name":"Xianfeng Tang","slug":"xianfeng-tang","given":"Xianfeng","family":"Tang","affiliation":"College of Information Sciences and Technology , The Pennsylvania State University , USA"},{"name":"Vasant Honavar","slug":"vasant-honavar","given":"Vasant","family":"Honavar","affiliation":""}],"abstract":"Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e.g., friendship, shared interests in music, etc.) between real-world individuals or entities. There is an urgent need for methods to obtain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. However, most of the work on multi-view learning focuses on data that lack a network structure, and most of the work on network embeddings has focused primarily on single-view networks. Against this background, we consider the multi-view network representation learning problem, i.e., the problem of constructing low-dimensional information preserving embeddings of multi-view networks. Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the information from the individual network views, while accounting for connectivity across (and hence complementarity of and correlations between) different views. The results of our experiments on two real-world multi-view data sets show that the embeddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks.","doi":"10.24963/ijcai.2019/489","url":"https://ijcai.org/proceedings/2019/489","pdf":"https://ijcai.org/proceedings/2019/0489.pdf","markdown":"https://ijcai.org/proceedings/2019/489.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/489","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":489,"pages":{"first":3527,"last":3533},"keywords":["Machine Learning Applications: Networks","Machine Learning: Data Mining","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Belkin and Niyogi, 2001] Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, volume 14, pages 585-591, 2001.","title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","authors":"Niyogi Belkin, Mikhail Belkin, Partha Niyogi","year":"2001","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bojchevski et al., 2018] Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, and Stephan Günnemann. Net- gan: Generating graphs via random walks. arXiv preprint arXiv:1803.00816, 2018.","title":"Netgan: Generating graphs via random walks","authors":"Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bui et al., 2016] Ngot Bui, Thanh Le, and Vasant Honavar. Labeling actors in multi-view social networks by integrat- ing information from within and across multiple views. In BigData, pages 616-625. IEEE, 2016.","title":"Labeling actors in multi-view social networks by integrating information from within and across multiple views","authors":"Bui","year":"2016","venue":"BigData","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cavallari et al., 2017] Sandro Cavallari, Vincent W Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, and Erik Cam- bria. Learning community embedding with community de- tection and node embedding on graphs. In CIKM, pages 377-386, 2017.","title":"Learning community embedding with community detection and node embedding on graphs","authors":"Sandro Cavallari, Hongyun Vincent W Zheng, Kevin Cai, Chang Chen-Chuan, Erik Cambria","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. In NIPS, pages 2672-2680, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of SIGKDD, pages 855-864. ACM, 2016.","title":"node2vec: Scalable feature learning for networks","authors":"Leskovec Grover, Aditya Grover, Jure Leskovec","year":"2016","venue":"Proceedings of SIGKDD","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hamilton et al., 2017] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems, pages 1024-1034, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[He et al., 2015] Kun He, Yiwei Sun, David Bindel, John Hopcroft, and Yixuan Li. Detecting overlapping commu- nities from local spectral subspaces. In ICDM, pages 769- 774. IEEE, 2015.","title":"Detecting overlapping communities from local spectral subspaces","authors":"Kun He, Yiwei Sun, David Bindel, John Hopcroft, Yixuan Li","year":"2015","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Huang et al., 2018] Jiaming Huang, Zhao Li, Vincent W. Zheng, Wen Wen, Yifan Yang, and Yuanmi Chen. Unsu- pervised multi-view nonlinear graph embedding. In UAI, pages 319-328, 2018.","title":"Unsupervised multi-view nonlinear graph embedding","authors":"Jiaming Huang, Zhao Li, Vincent W Zheng, Wen Wen, Yifan Yang, Yuanmi Chen","year":"2018","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kivelä et al., 2014] Mikko Kivelä, Alex Arenas, Marc Barthelemy, James P Gleeson, Yamir Moreno, and Ma- son A Porter. Multilayer networks. Journal of complex networks, 2(3):203-271, 2014.","title":"Multilayer networks","authors":"Mikko Kivelä, Alex Arenas, Marc Barthelemy, Yamir James P Gleeson, Mason A Moreno, Porter","year":"2014","venue":"Journal of complex networks","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ma et al., 2017] Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S Yu, Alex D Leow, and Ann B Ragin. Multi-view clustering with graph embedding for connectome analysis. In CIKM. ACM, 2017.","title":"Multi-view clustering with graph embedding for connectome analysis","authors":"Ma","year":"2017","venue":"CIKM. ACM","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Ma et al., 2019] Yao Ma, Suhang Wang, Chara C Aggarwal, Dawei Yin, and Jiliang Tang. Multi-dimensional graph convolutional networks. In SDM, pages 657-665. SIAM, 2019.","title":"Multi-dimensional graph convolutional networks","authors":"Yao Ma, Suhang Wang, Chara C Aggarwal, Dawei Yin, Jiliang Tang","year":"2019","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Maaten and Hinton, 2008] Laurens van der Maaten and Ge- offrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(Nov):2579-2605, 2008.","title":"Laurens van der Maaten and Geoffrey Hinton","authors":"Hinton Maaten","year":"2008","venue":"Journal of machine learning research","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ou et al., 2016] Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. Sigkdd. pages 1105-1114, 2016. Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social rep- resentations. In Proceedings of SIGKDD, pages 701-710. ACM, 2014.","title":"Deepwalk: Online learning of social representations","authors":"Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, Wenwu Zhu, ; Perozzi","year":"2014","venue":"Proceedings of SIGKDD","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Qu et al., 2017] Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, and Jiawei Han. An attention-based collaboration framework for multi-view network represen- tation learning. In CIKM, pages 1767-1776. ACM, 2017.","title":"An attention-based collaboration framework for multi-view network representation learning","authors":"Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Shi et al., 2016] Yu Shi, Myunghwan Kim, Shaunak Chat- terjee, Mitul Tiwari, Souvik Ghosh, and Rómer Rosales. Dynamics of large multi-view social networks: Synergy, cannibalization and cross-view interplay. In KDD, pages 1855-1864. ACM, 2016.","title":"Dynamics of large multi-view social networks: Synergy, cannibalization and cross-view interplay","authors":"Yu Shi, Myunghwan Kim, Shaunak Chatterjee, Mitul Tiwari, Souvik Ghosh, Rómer Rosales","year":"2016","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Tang et al., 2015] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large- scale information network embedding. In Proceedings of WWW, pages 1067-1077, 2015.","title":"Line: Largescale information network embedding","authors":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei","year":"2015","venue":"Proceedings of WWW","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Tang et al., 2016] Jian Tang, Jingzhou Liu, Ming Zhang, and Qiaozhu Mei. Visualizing large-scale and high- dimensional data. In Proceedings of WWW, pages 287- 297, 2016.","title":"Visualizing large-scale and highdimensional data","authors":"Jian Tang, Jingzhou Liu, Ming Zhang, Qiaozhu Mei","year":"2016","venue":"Proceedings of WWW","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Tu et al., 2018] Ke Tu, Peng Cui, Xiao Wang, Philip S Yu, and Wenwu Zhu. Deep recursive network embedding with regular equivalence. In Proceedings of SIGKDD, pages 2357-2366. ACM, 2018.","title":"Deep recursive network embedding with regular equivalence","authors":"Ke Tu, Peng Cui, Xiao Wang, Philip S Yu, Wenwu Zhu","year":"2018","venue":"Proceedings of SIGKDD","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2015] Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. On deep multi-view represen- tation learning. In International Conference on Machine Learning, pages 1083-1092, 2015.","title":"On deep multi-view representation learning","authors":"Wang","year":"2015","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2016a] Daixin Wang, Peng Cui, and Wenwu Zhu. Structural deep network embedding. In SIGKDD, pages 1225-1234, 2016.","title":"Structural deep network embedding","authors":"Wang","year":"2016","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2016b] Suhang Wang, Jiliang Tang, Charu Ag- garwal, and Huan Liu. Linked document embedding for classification. In CIKM, pages 115-124. ACM, 2016.","title":"Linked document embedding for classification","authors":"Wang","year":"2016","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2018] Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. Graphgan: graph representation learning with generative adversarial nets. In AAAI, 2018.","title":"Graphgan: graph representation learning with generative adversarial nets","authors":"Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yu et al., 2017] Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. Seqgan: Sequence generative adversarial nets with policy gradient. In AAAI, 2017.","title":"Seqgan: Sequence generative adversarial nets with policy gradient","authors":"Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Yu et al., 2018] Yanwei Yu, Huaxiu Yao, Hongjian Wang, Xianfeng Tang, and Zhenhui Li. Representation learning for large-scale dynamic networks. In International Con- ference on Database Systems for Advanced Applications, pages 526-541. Springer, 2018.","title":"Representation learning for large-scale dynamic networks","authors":"Yanwei Yu, Huaxiu Yao, Hongjian Wang, Xianfeng Tang, Zhenhui Li","year":"2018","venue":"International Conference on Database Systems for Advanced Applications","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhang et al., 2018a] Hongming Zhang, Liwei Qiu, Lingling Yi, and Yangqiu Song. Scalable multiplex network embed- ding. In IJCAI, pages 3082-3088, 2018.","title":"Scalable multiplex network embedding","authors":"Zhang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/428"},{"position":27,"raw":"[Zhang et al., 2018b] Ziwei Zhang, Peng Cui, Xiao Wang, Jian Pei, Xuanrong Yao, and Wenwu Zhu. Arbitrary-order proximity preserved network embedding. In SIGKDD, pages 2778-2786, 2018.","title":"Arbitrary-order proximity preserved network embedding","authors":"Zhang","year":"2018","venue":"SIGKDD","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}