Graph ladder networks for network classification

Ruiqi Hu, Shirui Pan, Jing Jiang, Guodong Long

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Numerous network representation-based algorithms for network classification have emerged in recent years, but many suffer from two limitations. First, they separate the network representation learning and node classification in networks into two steps, which may result in sub-optimal results because the node representation may not fit the classification model well, and vice versa. Second, they are mostly shallow methods that can only capture the linear and simple relationships in the data. In this paper, we propose an effective deep learning model, Graph Ladder Networks (GLN), for node classification in networks. Our model learns a ladder network which unifies the representation learning and network classification into one single framework by exploiting both labeled and unlabeled nodes in a network. To integrate both structure and node content information in the networks, the most recently developed graph convolution network, is further employed. The experiments on the most popular academic network dataset, Citeseer, demonstrate that our approach reaches outstanding performance compared to other state-of-the-art algorithms.

Original languageEnglish
Title of host publicationCIKM'17 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
Subtitle of host publicationNovember 6–10, 2017 Singapore, Singapore
EditorsShane Culpepper, Eric Lo, Joyce Ho
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages2103-2106
Number of pages4
ISBN (Electronic)9781450349185
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventACM International Conference on Information and Knowledge Management 2017 - Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017
Conference number: 26th
http://www.cikmconference.org/CIKM2017/

Conference

ConferenceACM International Conference on Information and Knowledge Management 2017
Abbreviated titleCIKM 2017
CountrySingapore
CitySingapore
Period6/11/1710/11/17
Internet address

Cite this

Hu, R., Pan, S., Jiang, J., & Long, G. (2017). Graph ladder networks for network classification. In S. Culpepper, E. Lo, & J. Ho (Eds.), CIKM'17 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management: November 6–10, 2017 Singapore, Singapore (pp. 2103-2106). New York NY USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3132847.3133124
Hu, Ruiqi ; Pan, Shirui ; Jiang, Jing ; Long, Guodong. / Graph ladder networks for network classification. CIKM'17 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management: November 6–10, 2017 Singapore, Singapore. editor / Shane Culpepper ; Eric Lo ; Joyce Ho. New York NY USA : Association for Computing Machinery (ACM), 2017. pp. 2103-2106
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title = "Graph ladder networks for network classification",
abstract = "Numerous network representation-based algorithms for network classification have emerged in recent years, but many suffer from two limitations. First, they separate the network representation learning and node classification in networks into two steps, which may result in sub-optimal results because the node representation may not fit the classification model well, and vice versa. Second, they are mostly shallow methods that can only capture the linear and simple relationships in the data. In this paper, we propose an effective deep learning model, Graph Ladder Networks (GLN), for node classification in networks. Our model learns a ladder network which unifies the representation learning and network classification into one single framework by exploiting both labeled and unlabeled nodes in a network. To integrate both structure and node content information in the networks, the most recently developed graph convolution network, is further employed. The experiments on the most popular academic network dataset, Citeseer, demonstrate that our approach reaches outstanding performance compared to other state-of-the-art algorithms.",
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Hu, R, Pan, S, Jiang, J & Long, G 2017, Graph ladder networks for network classification. in S Culpepper, E Lo & J Ho (eds), CIKM'17 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management: November 6–10, 2017 Singapore, Singapore. Association for Computing Machinery (ACM), New York NY USA, pp. 2103-2106, ACM International Conference on Information and Knowledge Management 2017, Singapore, Singapore, 6/11/17. https://doi.org/10.1145/3132847.3133124

Graph ladder networks for network classification. / Hu, Ruiqi; Pan, Shirui; Jiang, Jing; Long, Guodong.

CIKM'17 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management: November 6–10, 2017 Singapore, Singapore. ed. / Shane Culpepper; Eric Lo; Joyce Ho. New York NY USA : Association for Computing Machinery (ACM), 2017. p. 2103-2106.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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N2 - Numerous network representation-based algorithms for network classification have emerged in recent years, but many suffer from two limitations. First, they separate the network representation learning and node classification in networks into two steps, which may result in sub-optimal results because the node representation may not fit the classification model well, and vice versa. Second, they are mostly shallow methods that can only capture the linear and simple relationships in the data. In this paper, we propose an effective deep learning model, Graph Ladder Networks (GLN), for node classification in networks. Our model learns a ladder network which unifies the representation learning and network classification into one single framework by exploiting both labeled and unlabeled nodes in a network. To integrate both structure and node content information in the networks, the most recently developed graph convolution network, is further employed. The experiments on the most popular academic network dataset, Citeseer, demonstrate that our approach reaches outstanding performance compared to other state-of-the-art algorithms.

AB - Numerous network representation-based algorithms for network classification have emerged in recent years, but many suffer from two limitations. First, they separate the network representation learning and node classification in networks into two steps, which may result in sub-optimal results because the node representation may not fit the classification model well, and vice versa. Second, they are mostly shallow methods that can only capture the linear and simple relationships in the data. In this paper, we propose an effective deep learning model, Graph Ladder Networks (GLN), for node classification in networks. Our model learns a ladder network which unifies the representation learning and network classification into one single framework by exploiting both labeled and unlabeled nodes in a network. To integrate both structure and node content information in the networks, the most recently developed graph convolution network, is further employed. The experiments on the most popular academic network dataset, Citeseer, demonstrate that our approach reaches outstanding performance compared to other state-of-the-art algorithms.

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Hu R, Pan S, Jiang J, Long G. Graph ladder networks for network classification. In Culpepper S, Lo E, Ho J, editors, CIKM'17 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management: November 6–10, 2017 Singapore, Singapore. New York NY USA: Association for Computing Machinery (ACM). 2017. p. 2103-2106 https://doi.org/10.1145/3132847.3133124