Complex-Valued Embedding Models for Knowledge Graphs
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[1] J. Chang,et al. Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .
[2] Sameer Singh,et al. Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction , 2015, VS@HLT-NAACL.
[3] F. L. Hitchcock. The Expression of a Tensor or a Polyadic as a Sum of Products , 1927 .
[4] Nathan Srebro,et al. Global Optimality of Local Search for Low Rank Matrix Recovery , 2016, NIPS.
[5] P. Comon,et al. Tensor decompositions, alternating least squares and other tales , 2009 .
[6] Volker Tresp,et al. Logistic Tensor Factorization for Multi-Relational Data , 2013, ArXiv.
[7] Lars Schmidt-Thieme,et al. Predicting RDF triples in incomplete knowledge bases with tensor factorization , 2012, SAC '12.
[8] Noga Alon,et al. Sign rank versus VC dimension , 2015, COLT.
[9] Paul Mineiro,et al. Loss-Proportional Subsampling for Subsequent ERM , 2013, ICML.
[10] Joos Vandewalle,et al. A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..
[11] Lawrence K. Saul,et al. Modeling distances in large-scale networks by matrix factorization , 2004, IMC '04.
[12] Volker Tresp,et al. Type-Constrained Representation Learning in Knowledge Graphs , 2015, SEMWEB.
[13] Nathan Linial,et al. Complexity measures of sign matrices , 2007, Comb..
[14] Pedro M. Domingos,et al. Statistical predicate invention , 2007, ICML '07.
[15] Satya S. Sahoo,et al. A Survey of Current Approaches for Mapping of Relational Databases to RDF , 2009 .
[16] Nathan Srebro,et al. Beating SGD: Learning SVMs in Sublinear Time , 2011, NIPS.
[17] Augustin-Louis Cauchy,et al. Sur l'équation à l'aide de laquelle on détermine les inégalités séculaires des mouvements des planètes , 2009 .
[18] Guillaume Bouchard,et al. Complex Embeddings for Simple Link Prediction , 2016, ICML.
[19] Jason Weston,et al. Open Question Answering with Weakly Supervised Embedding Models , 2014, ECML/PKDD.
[20] Jason Weston,et al. Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.
[21] Emmanuel J. Candès,et al. A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..
[22] Yoshua Bengio,et al. Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model , 2008, IEEE Transactions on Neural Networks.
[23] Matthew D. Zeiler. ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.
[24] Alexa T. McCray,et al. An Upper-Level Ontology for the Biomedical Domain , 2003, Comparative and functional genomics.
[25] Huanbo Luan,et al. Modeling Relation Paths for Representation Learning of Knowledge Bases , 2015, EMNLP.
[26] Stephen Muggleton,et al. Inverse entailment and progol , 1995, New Generation Computing.
[27] Kai-Wei Chang,et al. Typed Tensor Decomposition of Knowledge Bases for Relation Extraction , 2014, EMNLP.
[28] Zhiyuan Liu,et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion , 2015, AAAI.
[29] Lars Schmidt-Thieme,et al. Pairwise interaction tensor factorization for personalized tag recommendation , 2010, WSDM '10.
[30] Mark W. Schmidt,et al. Hybrid Deterministic-Stochastic Methods for Data Fitting , 2011, SIAM J. Sci. Comput..
[31] Theodoros Rekatsinas,et al. Multi-relational Learning Using Weighted Tensor Decomposition with Modular Loss , 2013, ArXiv.
[32] Luc De Raedt,et al. Inductive Logic Programming: Theory and Methods , 1994, J. Log. Program..
[33] Phil Blunsom,et al. Reasoning about Entailment with Neural Attention , 2015, ICLR.
[34] Joshua B. Tenenbaum,et al. Modelling Relational Data using Bayesian Clustered Tensor Factorization , 2009, NIPS.
[35] Maximilian Nickel,et al. Tensor factorization for relational learning , 2013 .
[36] J. Kruskal. Rank, decomposition, and uniqueness for 3-way and n -way arrays , 1989 .
[37] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[38] Stan Matwin,et al. Text Classification Using WordNet Hypernyms , 1998, WordNet@ACL/COLING.
[39] Erhard Rahm,et al. Frameworks for entity matching: A comparison , 2010, Data Knowl. Eng..
[40] Rajarshi Das,et al. Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks , 2016, EACL.
[41] Christopher Potts,et al. A large annotated corpus for learning natural language inference , 2015, EMNLP.
[42] Chengfei Liu,et al. Query Evaluation on Probabilistic RDF Databases , 2009, WISE.
[43] Charles R. Johnson,et al. Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.
[44] Chris H. Q. Ding,et al. Binary Matrix Factorization with Applications , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).
[45] Lise Getoor,et al. Knowledge Graph Identification , 2013, SEMWEB.
[46] Heiner Stuckenschmidt,et al. RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models , 2013, AAAI.
[47] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[48] Luc De Raedt,et al. Statistical Relational Artificial Intelligence: Logic, Probability, and Computation , 2016, Statistical Relational Artificial Intelligence.
[49] Sebastian Riedel. Improving the Accuracy and Efficiency of MAP Inference for Markov Logic , 2008, UAI.
[50] Lise Getoor,et al. Learning Probabilistic Relational Models , 1999, IJCAI.
[51] Foster Provost,et al. Suspicion scoring based on guilt-by-association, colle ctive inference, and focused data access 1 , 2005 .
[52] Andrew McCallum,et al. Compositional Vector Space Models for Knowledge Base Completion , 2015, ACL.
[53] Y. Escoufier,et al. Analyse factorielle des matrices carrees non symetriques , 1980 .
[54] Yves Grandvalet,et al. Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases , 2016, J. Artif. Intell. Res..
[55] H. Hornich. Logik der Forschung , 1936 .
[56] H. Robbins. A Stochastic Approximation Method , 1951 .
[57] Sameer Singh,et al. Low-Dimensional Embeddings of Logic , 2014, ACL 2014.
[58] J. Neumann. Zur Algebra der Funktionaloperationen und Theorie der normalen Operatoren , 1930 .
[59] Ben Shneiderman,et al. D-Dupe: An Interactive Tool for Entity Resolution in Social Networks , 2006, 2006 IEEE Symposium On Visual Analytics Science And Technology.
[60] Robert J. Harrison,et al. Global arrays: A nonuniform memory access programming model for high-performance computers , 1996, The Journal of Supercomputing.
[61] Yehuda Koren,et al. Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.
[62] Jens Lehmann,et al. DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.
[63] Luc De Raedt,et al. Towards Combining Inductive Logic Programming with Bayesian Networks , 2001, ILP.
[64] Emmanuel J. Candès,et al. The Power of Convex Relaxation: Near-Optimal Matrix Completion , 2009, IEEE Transactions on Information Theory.
[65] Nicolas Le Roux,et al. A latent factor model for highly multi-relational data , 2012, NIPS.
[66] Yu Hu,et al. Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints , 2015, ACL.
[67] Estevam R. Hruschka,et al. Toward an Architecture for Never-Ending Language Learning , 2010, AAAI.
[68] Roman V. Yampolskiy,et al. AI-Complete, AI-Hard, or AI-Easy - Classification of Problems in AI , 2012, MAICS.
[69] Sameer Singh,et al. Injecting Logical Background Knowledge into Embeddings for Relation Extraction , 2015, NAACL.
[70] Inderjit S. Dhillon,et al. NOMAD: Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion , 2013, Proc. VLDB Endow..
[71] C. Lee Giles,et al. Autonomous citation matching , 1999, AGENTS '99.
[72] Daniel Jurafsky,et al. Distant supervision for relation extraction without labeled data , 2009, ACL.
[73] E. F. CODD,et al. A relational model of data for large shared data banks , 1970, CACM.
[74] Maximilian Nickel,et al. Complex and Holographic Embeddings of Knowledge Graphs: A Comparison , 2017, ArXiv.
[75] Peter J. Haas,et al. Large-scale matrix factorization with distributed stochastic gradient descent , 2011, KDD.
[76] Nicole Tourigny,et al. Bio2RDF: Towards a mashup to build bioinformatics knowledge systems , 2008, J. Biomed. Informatics.
[77] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[78] Santo Fortunato,et al. Community detection in graphs , 2009, ArXiv.
[79] Guillaume Bouchard,et al. On Approximate Reasoning Capabilities of Low-Rank Vector Spaces , 2015, AAAI Spring Symposia.
[80] M. Marelli,et al. SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment , 2014, *SEMEVAL.
[81] Piotr Indyk,et al. Enhanced hypertext categorization using hyperlinks , 1998, SIGMOD '98.
[82] Jason Weston,et al. Learning Structured Embeddings of Knowledge Bases , 2011, AAAI.
[83] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[84] Boris Polyak,et al. Acceleration of stochastic approximation by averaging , 1992 .
[85] Hans-Peter Kriegel,et al. A Three-Way Model for Collective Learning on Multi-Relational Data , 2011, ICML.
[86] Ben Taskar,et al. Probabilistic Classification and Clustering in Relational Data , 2001, IJCAI.
[87] Jason Weston,et al. Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks , 2015, ICLR.
[88] Evgeniy Gabrilovich,et al. A Review of Relational Machine Learning for Knowledge Graphs , 2015, Proceedings of the IEEE.
[89] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[90] Andrew McCallum,et al. Introduction to Statistical Relational Learning , 2007 .
[91] Jianfeng Gao,et al. Basic Reasoning with Tensor Product Representations , 2016, ArXiv.
[92] Edward Grefenstette,et al. Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors , 2013, *SEMEVAL.
[93] Steffen Rendle. Scaling Factorization Machines to Relational Data , 2013, Proc. VLDB Endow..
[94] Guillaume Bouchard,et al. A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion , 2016, AKBC@NAACL-HLT.
[95] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[96] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[97] Zhen Wang,et al. Knowledge Graph Embedding by Translating on Hyperplanes , 2014, AAAI.
[98] Danqi Chen,et al. Reasoning With Neural Tensor Networks for Knowledge Base Completion , 2013, NIPS.
[99] William Yang Wang,et al. Learning First-Order Logic Embeddings via Matrix Factorization , 2016, IJCAI.
[100] Tamara G. Kolda,et al. Scalable Tensor Factorizations with Missing Data , 2010, SDM.
[101] Antoine Bordes,et al. Effective Blending of Two and Three-way Interactions for Modeling Multi-relational Data , 2014, ECML/PKDD.
[102] Léon Bottou,et al. The Tradeoffs of Large Scale Learning , 2007, NIPS.
[103] Léon Bottou,et al. From machine learning to machine reasoning , 2011, Machine Learning.
[104] Guillaume Bouchard,et al. Iterative Splits of Quadratic Bounds for Scalable Binary Tensor Factorization , 2014, UAI.
[105] Tom M. Mitchell,et al. Random Walk Inference and Learning in A Large Scale Knowledge Base , 2011, EMNLP.
[106] Jennifer Neville,et al. Collective Classification with Relational Dependency Networks , 2003 .
[107] Yuji Matsumoto,et al. Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach , 2017, ArXiv.
[108] Mark Steedman,et al. Combined Distributional and Logical Semantics , 2013, TACL.
[109] Fabian M. Suchanek,et al. Fast rule mining in ontological knowledge bases with AMIE+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{docu , 2015, The VLDB Journal.
[110] Jason Weston,et al. Irreflexive and Hierarchical Relations as Translations , 2013, ArXiv.
[111] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[112] Peter Christen,et al. Data Matching , 2012, Data-Centric Systems and Applications.
[113] Jianfeng Gao,et al. Embedding Entities and Relations for Learning and Inference in Knowledge Bases , 2014, ICLR.
[114] Matthew Richardson,et al. Markov logic networks , 2006, Machine Learning.
[115] Eric Moulines,et al. Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning , 2011, NIPS.
[116] Lizhen Qu,et al. STransE: a novel embedding model of entities and relationships in knowledge bases , 2016, NAACL.
[117] Scott Aaronson,et al. Why Philosophers Should Care About Computational Complexity , 2011, Electron. Colloquium Comput. Complex..
[118] Xueyan Jiang,et al. Reducing the Rank in Relational Factorization Models by Including Observable Patterns , 2014, NIPS.
[119] Ashish Sabharwal,et al. Knowledge Completion for Generics using Guided Tensor Factorization , 2018, Transactions of the Association for Computational Linguistics.
[120] Thomas Demeester,et al. Lifted Rule Injection for Relation Embeddings , 2016, EMNLP.
[121] Gerhard Weikum,et al. YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia: Extended Abstract , 2013, IJCAI.
[122] John Miller,et al. Traversing Knowledge Graphs in Vector Space , 2015, EMNLP.
[123] Thomas Gottron,et al. Online dating recommender systems: the split-complex number approach , 2012, RSWeb@RecSys.
[124] Inderjit S. Dhillon,et al. Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems , 2012, 2012 IEEE 12th International Conference on Data Mining.
[125] Jason J. Jung,et al. Exploiting matrix factorization to asymmetric user similarities in recommendation systems , 2015, Knowl. Based Syst..
[126] Andrew McCallum,et al. Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema , 2016, EACL.
[127] Massimiliano Pontil,et al. A New Convex Relaxation for Tensor Completion , 2013, NIPS.
[128] Andrew McCallum,et al. Relation Extraction with Matrix Factorization and Universal Schemas , 2013, NAACL.
[129] Luc De Raedt,et al. Logical and relational learning , 2008, Cognitive Technologies.
[130] Volker Tresp,et al. Querying Factorized Probabilistic Triple Databases , 2014, SEMWEB.
[131] Lorenzo Rosasco,et al. Holographic Embeddings of Knowledge Graphs , 2015, AAAI.
[132] Jun Zhao,et al. Learning to Represent Knowledge Graphs with Gaussian Embedding , 2015, CIKM.
[133] Douglas B. Lenat,et al. CYC: a large-scale investment in knowledge infrastructure , 1995, CACM.
[134] Ramesh Nallapati,et al. Multi-instance Multi-label Learning for Relation Extraction , 2012, EMNLP.
[135] René Vidal,et al. Global Optimality in Tensor Factorization, Deep Learning, and Beyond , 2015, ArXiv.
[136] Omer Levy,et al. Do Supervised Distributional Methods Really Learn Lexical Inference Relations? , 2015, NAACL.
[137] Tengyu Ma,et al. Matrix Completion has No Spurious Local Minimum , 2016, NIPS.
[138] Jun Li,et al. A Link Prediction Approach for Item Recommendation with Complex Number , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).
[139] Lise Getoor,et al. Probabilistic Similarity Logic , 2010, UAI.
[140] P. Paatero,et al. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .
[141] Peter Haddawy,et al. Answering Queries from Context-Sensitive Probabilistic Knowledge Bases , 1997, Theor. Comput. Sci..
[142] Guillaume Bouchard,et al. Convex Collective Matrix Factorization , 2013, AISTATS.
[143] Wei Zhang,et al. Knowledge vault: a web-scale approach to probabilistic knowledge fusion , 2014, KDD.
[144] Lise Getoor,et al. Collective Classification in Network Data , 2008, AI Mag..
[145] P. Cameron. Naïve set theory , 1998 .
[146] Mark Dredze,et al. Entity Disambiguation for Knowledge Base Population , 2010, COLING.
[147] Seong-Bae Park,et al. A Translation-Based Knowledge Graph Embedding Preserving Logical Property of Relations , 2016, HLT-NAACL.
[148] Guillaume Bouchard,et al. Knowledge Graph Completion via Complex Tensor Factorization , 2017, J. Mach. Learn. Res..
[149] Francis R. Bach,et al. A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization , 2008, J. Mach. Learn. Res..
[150] Martin Chodorow,et al. Combining local context and wordnet similarity for word sense identification , 1998 .
[151] Luc De Raedt,et al. kLog: A Language for Logical and Relational Learning with Kernels (Extended Abstract) , 2012, IJCAI.
[152] Guillaume Bouchard,et al. On Inductive Abilities of Latent Factor Models for Relational Learning , 2017, J. Artif. Intell. Res..
[153] Praveen Paritosh,et al. Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.
[154] Michael I. Jordan,et al. Variational Bayesian Inference with Stochastic Search , 2012, ICML.
[155] Richard A. Harshman,et al. Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .
[156] Alex Graves,et al. Associative Long Short-Term Memory , 2016, ICML.
[157] Samy Bengio,et al. LLORMA: Local Low-Rank Matrix Approximation , 2016, J. Mach. Learn. Res..
[158] Gang Wang,et al. RC-NET: A General Framework for Incorporating Knowledge into Word Representations , 2014, CIKM.
[159] Geoffrey J. Gordon,et al. Relational learning via collective matrix factorization , 2008, KDD.
[160] Edwin R. Hancock,et al. Eigenspaces for Graphs , 2002, Int. J. Image Graph..
[161] Tim Rocktäschel,et al. Learning Knowledge Base Inference with Neural Theorem Provers , 2016, AKBC@NAACL-HLT.
[162] Euripides G. M. Petrakis,et al. Semantic similarity methods in wordNet and their application to information retrieval on the web , 2005, WIDM '05.
[163] Masashi Shimbo,et al. On the Equivalence of Holographic and Complex Embeddings for Link Prediction , 2017, ACL.
[164] Gerhard Weikum,et al. WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .
[165] N. Chino,et al. Complex Space Models for the Analysis of Asymmetry , 2002 .
[166] Mathias Niepert. Discriminative Gaifman Models , 2016, NIPS.
[167] Steffen Rendle,et al. Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.
[168] Ruslan Salakhutdinov,et al. On the quantitative analysis of deep belief networks , 2008, ICML '08.
[169] Joos Vandewalle,et al. Independent component analysis and (simultaneous) third-order tensor diagonalization , 2001, IEEE Trans. Signal Process..
[170] Junichi Yamagishi,et al. Initial investigation of speech synthesis based on complex-valued neural networks , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[171] David Heckerman,et al. Probabilistic Entity-Relationship Models, PRMs, and Plate Models , 2004 .
[172] Yang Liu,et al. Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention , 2016, ArXiv.
[173] Jason Weston,et al. A semantic matching energy function for learning with multi-relational data , 2013, Machine Learning.
[174] Leslie G. Valiant,et al. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata , 1993, Machine Learning: From Theory to Applications.
[175] Andrew McCallum,et al. Efficient clustering of high-dimensional data sets with application to reference matching , 2000, KDD '00.
[176] Andrew McCallum,et al. Structured Relation Discovery using Generative Models , 2011, EMNLP.
[177] Ruhi Sarikaya,et al. Knowledge Graph Inference for spoken dialog systems , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[178] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[179] Thomas Demeester,et al. Adversarial Sets for Regularising Neural Link Predictors , 2017, UAI.
[180] Guillaume Bouchard,et al. Decomposing Real Square Matrices via Unitary Diagonalization , 2016 .
[181] Michael Gamon,et al. Representing Text for Joint Embedding of Text and Knowledge Bases , 2015, EMNLP.
[182] Mason A. Porter,et al. Community Structure in Online Collegiate Social Networks , 2008 .
[183] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[184] Robert P. Goldman,et al. From knowledge bases to decision models , 1992, The Knowledge Engineering Review.
[185] Guillaume Bouchard,et al. Online Learning to Sample , 2015, 1506.09016.
[186] Lise Getoor,et al. Collective entity resolution in relational data , 2007, TKDD.
[187] Jing Xiao,et al. Non-negative matrix factorization as a feature selection tool for maximum margin classifiers , 2011, CVPR 2011.
[188] Y. Saad,et al. Numerical Methods for Large Eigenvalue Problems , 2011 .
[189] Jean-Loup Guillaume,et al. Fast unfolding of communities in large networks , 2008, 0803.0476.
[190] Steffen Staab,et al. TripleRank: Ranking Semantic Web Data by Tensor Decomposition , 2009, SEMWEB.
[191] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[192] Stephen J. Wright,et al. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent , 2011, NIPS.
[193] Christopher Potts,et al. Recursive Neural Networks Can Learn Logical Semantics , 2014, CVSC.
[194] Eric Moulines,et al. A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..
[195] Ryota Tomioka,et al. Estimation of low-rank tensors via convex optimization , 2010, 1010.0789.
[196] Bernardo A. Huberman,et al. E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations , 2005, Inf. Soc..
[197] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..