ABSTRACT
Attributed network embedding focuses on learning low-dimensional latent representations of nodes which can well preserve the original topological and node attributed proximity at the same time. Existing works usually assume that nodes with similar topology or similar attributes should also be close in the embedding space. This assumption ignores the phenomenon of partial correlation between network topological and node attributed similarities i.e. nodes with similar topology may be dissimilar in their attributes and vice versa. Partial correlation between the two information sources should be considered especially when there exist fraudulent edges (i.e., information from one source is vague) or unbalanced data distributions (i.e, topology structure similarity and node attribute similarity have different distributions). However, it is very challenging to consider the partial correlation between topology and attributes due to the heterogeneity of these two information sources. In this paper, we take partial correlation between topology and attributes into account and propose the Personalized Relation Ranking Embedding (PRRE) method for attributed networks which is capable of exploiting the partial correlation between node topology and attributes. The proposed PRRE model utilizes two thresholds to define different node relations and employs the Expectation-Maximization (EM) algorithm to learn these thresholds as well as other embedding parameters. Extensive experiments results on multiple real-world datasets show that the proposed PRRE model significantly outperforms the state-of-the-art methods in terms of various evaluation metrics.
- Lada A Adamic and Eytan Adar. 2003. Friends and neighbors on the web. Social networks 25, 3 (2003), 211--230.Google Scholar
- Philippe Apparicio, Mohamed Abdelmajid, Mylène Riva, and Richard Shearmur. 2008. Comparing alternative approaches to measuring the geographical accessibility of urban health services: Distance types and aggregation-error issues. International journal of health geographics 7, 1 (2008), 7.Google ScholarCross Ref
- Mukund Balasubramanian and Eric L Schwartz. 2002. The isomap algorithm and topological stability. Science 295, 5552 (2002), 7--7.Google Scholar
- Mikhail Belkin and Partha Niyogi. 2002. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in neural information processing systems. 585--591. Google ScholarDigital Library
- Aleksandar Bojchevski and Stephan Günnemann. 2018. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. In International Conference on Learning Representations. https://openreview.net/forum?id=r1ZdKJ-0WGoogle Scholar
- Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015. Grarep: Learning graph representations with global structural information. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 891--900. Google ScholarDigital Library
- Dániel Fogaras, Balázs Rácz, Károly Csalogány, and Tamás Sarlós. 2005. Towards scaling fully personalized pagerank: Algorithms, lower bounds, and experiments. Internet Mathematics 2, 3 (2005), 333--358.Google ScholarCross Ref
- 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. 1025--1035.Google Scholar
- Xiao Huang, Jundong Li, and Xia Hu. 2017. Accelerated attributed network embedding. In Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, 633--641.Google ScholarCross Ref
- Caiyan Jia, Yafang Li, Matthew B Carson, Xiaoyang Wang, and Jian Yu. 2017. Node Attribute-enhanced Community Detection in Complex Networks. Scientific Reports 7, 1 (2017), 2626.Google ScholarCross Ref
- Ling Jian, Jundong Li, Kai Shu, and Huan Liu. 2016. Multi-Label Informed Feature Selection.. In IJCAI. 1627--1633. Google ScholarDigital Library
- Omer Levy and Yoav Goldberg. 2014. Neural word embedding as implicit matrix factorization. In Advances in neural information processing systems. 2177--2185. Google ScholarDigital Library
- Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, and Huan Liu. 2017. Attributed network embedding for learning in a dynamic environment. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 387--396. Google ScholarDigital Library
- Jiongqian Liang, Peter Jacobs, and Srinivasan Parthasarathy. 2017. SEANO: semi-supervised embedding in attributed networks with outliers. arXiv preprint arXiv:1703.08100 (2017).Google Scholar
- Jiongqian Liang, Peter Jacobs, Jiankai Sun, and Srinivasan Parthasarathy. 2017. Semi-supervised Embedding in Attributed Networks with Outliers. arXiv preprint arXiv:1703.08100 (2017).Google Scholar
- Lizi Liao, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2017. Attributed social network embedding. arXiv preprint arXiv:1705.04969 (2017).Google Scholar
- Ben London and Lise Getoor. 2014. Collective Classification of Network Data. Data Classification: Algorithms and Applications 399 (2014).Google Scholar
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579--2605.Google Scholar
- Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. 2000. Automating the construction of internet portals with machine learning. Information Retrieval 3, 2 (2000), 127--163. 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 Advances in neural information processing systems. 3111--3119. Google ScholarDigital Library
- Suphakit Niwattanakul, Jatsada Singthongchai, Ekkachai Naenudorn, and Supachanun Wanapu. 2013. Using of Jaccard coefficient for keywords similarity. In Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol. 1.Google Scholar
- Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. 2016. Asymmetric transitivity preserving graph embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1105--1114. Google ScholarDigital Library
- Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang. 2016. Triparty deep network representation. Network 11, 9 (2016), 12.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
- Joseph J Pfeiffer III, Sebastian Moreno, Timothy La Fond, Jennifer Neville, and Brian Gallagher. 2014. Attributed graph models: Modeling network structure with correlated attributes. In Proceedings of the 23rd international conference on World wide web. ACM, 831--842. 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
- Sam T Roweis and Lawrence K Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. science 290, 5500 (2000), 2323--2326.Google Scholar
- 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. 2009. 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
- Cunchao Tu, Han Liu, Zhiyuan Liu, and Maosong Sun. 2017. Cane: Contextaware network embedding for relation modeling. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 1722--1731.Google ScholarCross Ref
- 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
- Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and PierreAntoine Manzagol. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research 11, Dec (2010), 3371--3408. 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
- Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. 2017. Community Preserving Network Embedding.. In AAAI. 203--209.Google Scholar
- Xin Wang, Roger Donaldson, Christopher Nell, Peter Gorniak, Martin Ester, and Jiajun Bu. 2016. Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization.. In AAAI. 1331--1337. Google ScholarDigital Library
- Xin Wang, Steven C.H. Hoi, Martin Ester, Jiajun Bu, and Chun Chen. 2017. Learning Personalized Preference of Strong and Weak Ties for Social Recommendation. In Proceedings of the 26th International Conference on World Wide Web (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1601--1610. Google ScholarDigital Library
- Xin Wang, Wei Lu, Martin Ester, Can Wang, and Chun Chen. 2016. Social recommendation with strong and weak ties. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 5--14. 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
- Jaewon Yang, Julian McAuley, and Jure Leskovec. 2013. Community detection in networks with node attributes. In Data Mining (ICDM), 2013 IEEE 13th international conference on. IEEE, 1151--1156.Google ScholarCross Ref
- Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov. 2016. Revisiting semisupervised 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 Pacific-Asia conference on knowledge discovery and data mining. Springer, 183--195.Google ScholarCross Ref
- Si Zhang and Hanghang Tong. 2016. Final: Fast attributed network alignment. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1345--1354. Google ScholarDigital Library
- Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, and Can Wang. 2018. ANRL: Attributed Network Representation Learning via Deep Neural Networks.. In IJCAI. 3155--3161.Google Scholar
Index Terms
- PRRE: Personalized Relation Ranking Embedding for Attributed Networks
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