Ahmed Rashed
Lars Schmidt-Thieme
ABSTRACT
The task of classifying multi-relational data spans a wide range of domains such as document classification in citation networks, classification of emails, and protein labeling in proteins interaction graphs. Current state-of-the-art classification models rely on learning per-entity latent representations by mining the whole structure of the relations' graph, however, they still face two major problems. Firstly, it is very challenging to generate expressive latent representations in sparse multi-relational settings with implicit feedback relations as there is very little information per-entity. Secondly, for entities with structured properties such as titles and abstracts (text) in documents, models have to be modified ad-hoc. In this paper, we aim to overcome these two main drawbacks by proposing a flexible nonlinear latent embedding model (BRNLE) for the classification of multi-relational data. The proposed model can be applied to entities with structured properties such as text by utilizing the numerical vector representations of those properties. To address the sparsity problem of implicit feedback relations, the model is optimized via a sparsely-regularized multi-relational pair-wise Bayesian personalized ranking loss (BPR). Experiments on four different real-world datasets show that the proposed model significantly outperforms state-of-the-art models for multi-relational classification.
- Bobby-Joe Breitkreutz, Chris Stark, Teresa Reguly, Lorrie Boucher, Ashton Breitkreutz, Michael Livstone, Rose Oughtred, Daniel H Lackner, Jürg Bahler, Valerie Wood, et almbox. 2007. The BioGRID interaction database: 2008 update. Nucleic acids research, Vol. 36, suppl_1 (2007), D637--D640.Google Scholar
- Hongyun Cai, Vincent W Zheng, and Kevin Chang. 2018. A comprehensive survey of graph embedding: problems, techniques and applications. IEEE Transactions on Knowledge and Data Engineering (2018).Google Scholar
- Savs o Dvz eroski. 2003. Multi-relational data mining: an introduction. ACM SIGKDD Explorations Newsletter, Vol. 5, 1 (2003), 1--16. Google ScholarDigital Library
- Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep Sparse Rectifier Neural Networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research), , Geoffrey Gordon, David Dunson, and Miroslav Dudík (Eds.), Vol. 15. PMLR, Fort Lauderdale, FL, USA, 315--323. http://proceedings.mlr.press/v15/glorot11a.htmlGoogle Scholar
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 855--864. Google ScholarDigital Library
- Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034. Google ScholarDigital Library
- Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on Recommender systems. ACM, 135--142. Google ScholarDigital Library
- Artus Krohn-Grimberghe, Lucas Drumond, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2012. Multi-relational matrix factorization using bayesian personalized ranking for social network data. In Proceedings of the fifth ACM international conference on Web search and data mining. ACM, 173--182. Google ScholarDigital Library
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).Google Scholar
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 701--710. Google ScholarDigital Library
- Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . ACM, 459--467. Google ScholarDigital Library
- Steffen Rendle and Christoph Freudenthaler. 2014. Improving pairwise learning for item recommendation from implicit feedback. In Proceedings of the 7th ACM international conference on Web search and data mining. ACM, 273--282. Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, 452--461. Google ScholarDigital Library
- Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine , Vol. 29, 3 (2008), 93.Google Scholar
- Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee. 2016. Understanding and improving convolutional neural networks via concatenated rectified linear units. In International Conference on Machine Learning . 2217--2225. Google ScholarDigital Library
- Ajit P Singh and Geoffrey J Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 650--658. 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 the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1067--1077. Google ScholarDigital Library
- Lei Tang and Huan Liu. 2009a. Relational learning via latent social dimensions. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 817--826. Google ScholarDigital Library
- Lei Tang and Huan Liu. 2009b. Scalable learning of collective behavior based on sparse social dimensions. In Proceedings of the 18th ACM conference on Information and knowledge management. ACM, 1107--1116. Google ScholarDigital Library
- N Kipf Thomas and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations .Google Scholar
- Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, and Maosong Sun. 2016. Max-Margin DeepWalk: Discriminative Learning of Network Representation.. In IJCAI . 3889--3895. Google ScholarDigital Library
- Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1225--1234. Google ScholarDigital Library
- Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang. 2015. Network representation learning with rich text information.. In IJCAI . 2111--2117. Google ScholarDigital Library
- Hao Yin, Austin R Benson, Jure Leskovec, and David F Gleich. 2017. Local higher-order graph clustering. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 555--564. Google ScholarDigital Library
Index Terms
- Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings
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