ABSTRACT
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. While achieving competitive performance on a variety of network inference tasks such as node classification and link prediction, these methods treat the relations between nodes as a binary variable and ignore the rich semantics of edges. In this work, we attempt to learn network embeddings which simultaneously preserve network structure and relations between nodes. Experiments on several real-world networks illustrate that by considering different relations between different node pairs, our method is capable of producing node embeddings of higher quality than a number of state-of-the-art network embedding methods, as evaluated on a challenging multi-label node classification task.
- Sami Abu-El-Haija, Bryan Perozzi, and Rami Al-Rfou. 2017. Learning edge representations via low-rank asymmetric projections. In Proceedings of CIKM . ACM, 1787--1796. Google ScholarDigital Library
- David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. JMLR , Vol. 3, Jan (2003), 993--1022. Google ScholarDigital Library
- Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. 2008. LIBLINEAR: A library for large linear classification. JMLR , Vol. 9 (2008), 1871--1874. Google ScholarDigital Library
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In Proceedings of KDD . Google ScholarDigital Library
- Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS. 1025--1035.Google Scholar
- Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of WWW. 507--517. Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR .Google Scholar
- Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In AAAI , Vol. 15. 2181--2187. Google ScholarDigital Library
- Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of SIGIR. ACM, 43--52. Google ScholarDigital Library
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS . 3111--3119. Google ScholarDigital Library
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of KDD. ACM, 701--710. Google ScholarDigital Library
- Ryan A Rossi, Rong Zhou, and Nesreen K Ahmed. 2018. Deep Inductive Network Representation Learning. In Companion of the Web Conference 2018 . 953--960. Google ScholarDigital Library
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In Proceedings of WWW. International World Wide Web Conferences Steering Committee, 1067--1077. Google ScholarDigital Library
- Cunchao Tu, Zhengyan Zhang, Zhiyuan Liu, and Maosong Sun. 2017. TransNet: translation-based network representation learning for social relation extraction. In Proceedings of IJCAI . Google ScholarDigital Library
- Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural Deep Network Embedding. In Proceedings of KDD . Google ScholarDigital Library
- Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov. 2016. Revisiting semi-supervised learning with graph embeddings. arXiv preprint arXiv:1603.08861 (2016). Google ScholarDigital Library
- Shuhan Yuan, Xintao Wu, and Yang Xiang. 2017. SNE: signed network embedding. In PAKDD. Springer, 183--195.Google Scholar
Index Terms
- Enhanced Network Embeddings via Exploiting Edge Labels
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