Reducing uncertainty of dynamic heterogeneous information networks: a fusing reconstructing approach

Ning Yang1, Lifang He2, Zheng Li1, Philip S. Yu3
1School of Computer Science, Sichuan University, Chengdu, China
2Computer Vision Institute, Shenzhen University, Shenzhen, China
3Department of Computer Science, University of Illinois at Chicago, Chicago, USA

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Achlioptas D, Mcsherry F (2007) Fast computation of low-rank matrix approximations. J ACM (JACM) 54(2):9

Ahmed A, Shervashidze N, Narayanamurthy S, Josifovski V (2013) Smo: Distributed large-scale natural graph factorization. In: Proceedings of the 22nd international conference on World Wide Web, WWW ’13. International World Wide Web Conferences Steering Committee, pp 37–48

Anandkumar A, Ge R, Hsu D, Kakade SM, Telgarsky M (2014) Tensor decompositions for learning latent variable models. J Mach Learn Res 15(1):2773–283

Bao J, Zheng Y, Mokbel MF (2012) Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th international conference on advances in geographic information systems, SIGSPATIAL ’12. ACM, New York, pp 199–208. doi: 10.1145/2424321.2424348

Brand M (2003) Continuous nonlinear dimensionality reduction by kernel eigenmaps. In: IJCAI, pp 547–554

Cao S, Lu W, Xu Q (2015) Grarep: Learning graph representations with global structural information. In: Proceedings of the 24th ACM international on conference on information and knowledge management, CIKM ’15. ACM, pp 891–900

Chang S, Han W, Tang J, Qi GJ, Aggarwal CC, Huang TS (2015) Heterogeneous network embedding via deep architectures. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’15. ACM, pp 119–128

Chen J, Saad Y (2009) On the tensor svd and the optimal low rank orthogonal approximation of tensors. SIAM J Matrix Anal Appl 30(4):1709–1734

Goldfarb D, Qin Z (2014) Robust low-rank tensor recovery: models and algorithms. SIAM J Matrix Anal Appl 35(1):225–253

Golub GH, Van Loan CF (2013) Matrix computations, vol 3. JHU Press, Baltimore

He X, Cai D, Yan S, Zhang HJ (2005) Neighborhood preserving embedding. In: Tenth IEEE international conference on computer vision, 2005. ICCV 2005, vol 2. IEEE, pp 1208–1213

Jia C, Zhong G, Fu Y (2014) Low-rank tensor learning with discriminant analysis for action classification and image recovery. In: Twenty-eighth AAAI conference on artificial intelligence

Koch O, Lubich C (2010) Dynamical tensor approximation. SIAM J Matrix Anal Appl 31(5):2360–2375

Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500

Kong X, Yu PS, Ding Y, Wild DJ (2012) Meta path-based collective classification in heterogeneous information networks. In: Proceedings of the 21st ACM international conference on information and knowledge management, CIKM ’12. ACM, pp 1567–1571

Kruskal JB (1989) Rank, decomposition, and uniqueness for 3-way and n-way arrays. Multiway Data Anal 33:7–18

Liu J, Musialski P, Wonka P, Ye J (2013) Tensor completion for estimating missing values in visual data. IEEE Trans Pattern Anal Mach Intell 35(1):208–220

Lubich C, Rohwedder T, Schneider R, Vandereycken B (2013) Dynamical approximation by hierarchical tucker and tensor-train tensors. SIAM J Matrix Anal Appl 34(2):470–494

Ou M, Cui P, Pei J, Zhang Z, Zhu W (2016) Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’16. ACM, New York, pp 1105–1114. doi: 10.1145/2939672.2939751

Phan AH, Cichocki A (2011) Parafac algorithms for large-scale problems. Neurocomputing 74(11):1970–1984

Shaw B, Jebara T (2009) Structure preserving embedding. In: Proceedings of the 26th annual international conference on machine learning. ACM, pp 937–944

Shi C, Kong X, Yu PS, Xie S, Wu B (2012) Relevance search in heterogeneous networks. In: Proceedings of the 15th international conference on extending database technology, EDBT ’12. ACM, pp 180–191

Sun J, Tao D, Papadimitriou S, Yu PS, Faloutsos C (2008) Incremental tensor analysis: theory and applications. ACM Trans Knowl Discov Data 2(3):11:1–11:37

Sun Y, Han J, Aggarwal CC, Chawla NV (2012) When will it happen?: Relationship prediction in heterogeneous information networks. In: Proceedings of the fifth ACM international conference on web search and data mining, WSDM ’12. ACM, pp 663–672

Sun Y, Norick B, Han J, Yan X, Yu PS, Yu X (2012) Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’12. ACM, pp 1348–1356

Sun Y, Yu Y, Han J (2009) Ranking-based clustering of heterogeneous information networks with star network schema. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’09. ACM, pp 797–806

Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th international conference on World Wide Web, WWW’15. International World Wide Web Conferences Steering Committee, pp 1067–1077

Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’16. ACM, New York, pp 1225–1234. doi: 10.1145/2939672.2939753

Wang Y, Zheng Y, Xue Y (2014) Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’14. ACM, pp 25–34

Wen Z, Yin W (2013) A feasible method for optimization with orthogonality constraints. Math Program 142(1–2):397–434

Xiong Y, Zhu Y, Yu P (2015) Top-k similarity join in heterogeneous information networks. IEEE Trans Knowl Data Eng 27(6):1710–1723

Yan S, Xu D, Zhang B, Zhang HJ, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

Yang Y, Chawla N, Sun Y, Hani J (2012) Predicting links in multi-relational and heterogeneous networks. In: 2012 IEEE 12th international conference on data mining (ICDM). IEEE, pp 755–764

Yu Y, Cheng H, Zhang X (2014) Approximate low-rank tensor learning. In: 7th NIPS workshop on optimization for machine learning

Yuan Z, Sang J, Liu Y, Xu C (2013) Latent feature learning in social media network. In: Proceedings of the 21st ACM international conference on multimedia, MM’13. ACM, pp 253–262

Zheng Y, Liu F, Hsieh H.P (2013) U-air: When urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’13. ACM, pp 1436–1444

Zheng Y, Liu T, Wang Y, Zhu Y, Liu Y, Chang E (2014) Diagnosing new york city’s noises with ubiquitous data. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 715–725

Zhou Y, Liu L (2013) Social influence based clustering of heterogeneous information networks. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’13. ACM, pp 338–346