Yao Ma
ABSTRACT
Information networks are ubiquitous in many applications. A popular way to facilitate the information in a network is to embed the network structure into low-dimension spaces where each node is represented as a vector. The learned representations have been proven to advance various network analysis tasks such as link prediction and node classification. The majority of existing embedding algorithms are designed for the networks with one type of nodes and one dimension of relations among nodes. However, many networks in the real-world complex systems have multiple types of nodes and multiple dimensions of relations. For example, an e-commerce network can have users and items, and items can be viewed or purchased by users, corresponding to two dimensions of relations. In addition, some types of nodes can present hierarchical structure. For example, authors in publication networks are associated to affiliations; and items in e-commerce networks belong to categories. Most of existing methods cannot be naturally applicable to these networks. In this paper, we aim to learn representations for networks with multiple dimensions and hierarchical structure. In particular, we provide an approach to capture independent information from each dimension and dependent information across dimensions and propose a framework MINES, which performs Multi-dImension Network Embedding with hierarchical Structure. Experimental results on a network from a real-world e-commerce website demonstrate the effectiveness of the proposed framework.
- Mikhail Belkin and Partha Niyogi . 2002. Laplacian eigenmaps and spectral techniques for embedding and clustering Advances in neural information processing systems. 585--591. Google Scholar
Digital Library
- Michael GH Bell, Yasunori Iida, et almbox. . 1997. Transportation network analysis. (1997).Google Scholar
- Michele Berlingerio, Michele Coscia, Fosca Giannotti, Anna Monreale, and Dino Pedreschi . 2013 a. Multidimensional networks: foundations of structural analysis. World Wide Web, Vol. 16, 5--6 (2013), 567--593. Google ScholarDigital Library
- Michele Berlingerio, Fabio Pinelli, and Francesco Calabrese . 2013 b. Abacus: frequent pattern mining-based community discovery in multidimensional networks. Data Mining and Knowledge Discovery Vol. 27, 3 (2013), 294--320.Google ScholarCross Ref
- Smriti Bhagat, Graham Cormode, and S Muthukrishnan . 2011. Node classification in social networks. Social network data analytics. Springer, 115--148. Google ScholarDigital Library
- Ronald L Breiger, Scott A Boorman, and Phipps Arabie . 1975. An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. Journal of mathematical psychology Vol. 12, 3 (1975), 328--383.Google ScholarCross Ref
- Carter T Butts . 2009. Revisiting the foundations of network analysis. science, Vol. 325, 5939 (2009), 414--416.Google Scholar
- Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C Aggarwal, and Thomas S Huang . 2015. Heterogeneous network embedding via deep architectures Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 119--128. Google ScholarDigital Library
- Ting Chen and Yizhou Sun . 2017. Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, United Kingdom, February 6--10, 2017, bibfieldeditorMaarten de Rijke, Milad Shokouhi, Andrew Tomkins, and Min Zhang (Eds.). ACM, 295--304. Google ScholarDigital Library
- Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu . 2017. A Survey on Network Embedding. arXiv preprint arXiv:1711.08752 (2017).Google Scholar
- Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami . 2017. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. (2017).Google Scholar
- Palash Goyal and Emilio Ferrara . 2017. Graph Embedding Techniques, Applications, and Performance: A Survey. arXiv preprint arXiv:1705.02801 (2017).Google Scholar
- Aditya Grover and Jure Leskovec . 2016. node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 855--864. Google ScholarDigital Library
- Gueorgi Kossinets and Duncan J Watts . 2006. Empirical analysis of an evolving social network. science, Vol. 311, 5757 (2006), 88--90.Google Scholar
- Richard A Kronmal and Arthur V Peterson Jr . 1979. On the alias method for generating random variables from a discrete distribution. The American Statistician Vol. 33, 4 (1979), 214--218.Google ScholarCross Ref
- Ka-Wei Roy Lee and Ee-Peng Lim . 2016. Friendship maintenance and prediction in multiple social networks Proceedings of the 27th ACM Conference on Hypertext and Social Media. ACM, 83--92. 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. arXiv preprint arXiv:1706.01860 (2017).Google Scholar
- David Liben-Nowell and Jon M. Kleinberg . 2003. The link prediction problem for social networks. Proceedings of the 2003 ACM CIKM International Conference on Information and Knowledge Management, New Orleans, Louisiana, USA, November 2--8, 2003. ACM, 556--559. Google ScholarDigital Library
- Chih-Jen Lin . 2007. Projected gradient methods for nonnegative matrix factorization. Neural computation, Vol. 19, 10 (2007), 2756--2779. Google ScholarDigital Library
- Yao Ma, Suhang Wang, ZhaoChun Ren, Dawei Yin, and Jiliang Tang . 2017. Preserving Local and Global Information for Network Embedding. arXiv preprint arXiv:1710.07266 (2017).Google Scholar
- Laurens van der Maaten and Geoffrey Hinton . 2008. Visualizing data using t-SNE. Journal of Machine Learning Research Vol. 9, Nov (2008), 2579--2605.Google Scholar
- MARGGF Magnani, Anna Monreale, Giulio Rossetti, and Fosca Giannotti . 2013. On multidimensional network measures. In Italian conference on Sistemi Evoluti per le Basi di Dati (SEBD).Google Scholar
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean . 2013 a. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).Google Scholar
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean . 2013 b. Efficient Estimation of Word Representations in Vector Space. CoRR Vol. abs/1301.3781 (2013).Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean . 2013 c. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119. Google ScholarDigital Library
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena . 2014. DeepWalk: online learning of social representations The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, New York, NY, USA - August 24 - 27, 2014, bibfieldeditorSofus A. Macskassy, Claudia Perlich, Jure Leskovec, Wei Wang, and Rayid Ghani (Eds.). ACM, 701--710. Google ScholarDigital Library
- Giulio Rossetti, Michele Berlingerio, and Fosca Giannotti . 2011. Scalable link prediction on multidimensional networks Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on. IEEE, 979--986. Google ScholarDigital Library
- Jian Tang, Jingzhou Liu, Ming Zhang, and Qiaozhu Mei . 2016. Visualization Large-scale and High-dimensional Data. CoRR Vol. abs/1602.00370 (2016). Google ScholarDigital Library
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei . 2015. LINE: Large-scale Information Network Embedding. Proceedings of the 24th International Conference on World Wide Web, WWW 2015, Florence, Italy, May 18--22, 2015, bibfieldeditorAldo Gangemi, Stefano Leonardi, and Alessandro Panconesi (Eds.). ACM, 1067--1077. Google ScholarDigital Library
- Joshua B Tenenbaum, Vin De Silva, and John C Langford . 2000. A global geometric framework for nonlinear dimensionality reduction. science, Vol. 290, 5500 (2000), 2319--2323.Google Scholar
- Suhang Wang, Charu C. Aggarwal, Jiliang Tang, and Huan Liu . {n. d.}. Attributed Signed Network Embedding. In Proceedings of CIKM. Google ScholarDigital Library
- Suhang Wang, Jiliang Tang, Charu C. Aggarwal, Yi Chang, and Huan Liu . 2017. Signed Network Embedding in Social Media. In Proceedings of SDM. 327--335.Google ScholarCross Ref
- Suhang Wang, Jiliang Tang, Charu C. Aggarwal, and Huan Liu . 2016. Linked Document Embedding for Classification. In Proceedings of CIKM. 115--124. Google ScholarDigital Library
- Stanley Wasserman and Katherine Faust . 1994. Social network analysis: Methods and applications. Vol. Vol. 8. Cambridge university press.Google Scholar
- Barry Wellman . 1983. Network analysis: Some basic principles. Sociological theory (1983), 155--200.Google Scholar
- Multi-Dimensional Network Embedding with Hierarchical Structure
Recommendations
Cross-Network Embedding for Multi-Network Alignment
WWW '19: The World Wide Web ConferenceRecently, data mining through analyzing the complex structure and diverse relationships on multi-network has attracted much attention in both academia and industry. One crucial prerequisite for this kind of multi-network mining is to map the nodes ...
Multi-view network embedding with node similarity ensemble
AbstractNode similarity is utilized as the most popular guidance for network embedding: nodes more similar in a network should still be more similar when mapping node information from a high-dimensional vector space to a low-dimensional vector space. Most ...
Weighted ego network for forming hierarchical structure of road networks
Studies on the structural properties of road network and its close relationship with the traffic flow distribution have received intensive interdisciplinary attention. However, most of these attempts were theoretical. It is also a challenge to ...
Comments