Tensor decomposition for analysing time-evolving social networks: an overview

[1]  Ricardo Baeza-Yates,et al.  Scalable Dynamic Graph Summarization , 2020, IEEE Transactions on Knowledge and Data Engineering.

[2]  Hadi Fanaee-T,et al.  Evolving Social Networks Analysis via Tensor Decompositions: From Global Event Detection Towards Local Pattern Discovery and Specification , 2019, DS.

[3]  Maja Pantic,et al.  TensorLy: Tensor Learning in Python , 2016, J. Mach. Learn. Res..

[4]  G. Michailidis,et al.  Interconnectedness in the Interbank Market , 2015, Journal of Financial Economics.

[5]  Martin T. Wells,et al.  rTensor: An R Package for Multidimensional Array (Tensor) Unfolding, Multiplication, and Decomposition , 2018 .

[6]  João Gama,et al.  Social network analysis: An overview , 2018, WIREs Data Mining Knowl. Discov..

[7]  Petko Bogdanov,et al.  LARC: Learning Activity-Regularized Overlapping Communities Across Time , 2018, KDD.

[8]  Hadi Fanaee-T,et al.  Dynamic graph summarization: a tensor decomposition approach , 2018, Data Mining and Knowledge Discovery.

[9]  Evangelos E. Papalexakis,et al.  Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition , 2018, ECML/PKDD.

[10]  Kamran Paynabar,et al.  Change detection in a dynamic stream of attributed networks , 2017, Journal of Quality Technology.

[11]  Georgios B. Giannakis,et al.  Identification of Overlapping Communities via Constrained Egonet Tensor Decomposition , 2017, IEEE Transactions on Signal Processing.

[12]  Giulio Rossetti,et al.  Community Discovery in Dynamic Networks , 2017, ACM Comput. Surv..

[13]  Danai Koutra,et al.  Graph Summarization Methods and Applications , 2016, ACM Comput. Surv..

[14]  Raphael H. Heiberger,et al.  Predicting economic growth with stock networks , 2018 .

[15]  Jiannong Cao,et al.  Fast Tensor Factorization for Accurate Internet Anomaly Detection , 2017, IEEE/ACM Transactions on Networking.

[16]  Christos Faloutsos,et al.  TensorCast: Forecasting with Context Using Coupled Tensors (Best Paper Award) , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[17]  Sofia da Silva Fernandes,et al.  The Initialization and Parameter Setting Problem in Tensor Decomposition-Based Link Prediction , 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[18]  Di Dong,et al.  Evolutionary Nonnegative Matrix Factorization Algorithms for Community Detection in Dynamic Networks , 2017, IEEE Transactions on Knowledge and Data Engineering.

[19]  Mahmood Al-khassaweneh,et al.  A tensor based framework for community detection in dynamic networks , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Sarvapali D. Ramchurn,et al.  Algorithms for Graph-Constrained Coalition Formation in the Real World , 2017, TIST.

[21]  Nikos D. Sidiropoulos,et al.  Tensors for Data Mining and Data Fusion , 2016, ACM Trans. Intell. Syst. Technol..

[22]  Nikos D. Sidiropoulos,et al.  Tensor Decomposition for Signal Processing and Machine Learning , 2016, IEEE Transactions on Signal Processing.

[23]  Christos Faloutsos,et al.  TensorCast : Forecasting with Context using Coupled Tensors , 2017 .

[24]  Jungwoo Lee,et al.  BIGtensor: Mining Billion-Scale Tensor Made Easy , 2016, CIKM.

[25]  João Gama,et al.  Event detection from traffic tensors: A hybrid model , 2016, Neurocomputing.

[26]  Lee Sael,et al.  SCouT: Scalable coupled matrix-tensor factorization - algorithm and discoveries , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[27]  Hadi Fanaee-T,et al.  Tensor-based anomaly detection: An interdisciplinary survey , 2016, Knowl. Based Syst..

[28]  Jieping Ye,et al.  Detection of number of components in CANDECOMP/PARAFAC models via minimum description length , 2016, Digit. Signal Process..

[29]  Tamara G. Kolda,et al.  Parallel Tensor Compression for Large-Scale Scientific Data , 2015, 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[30]  Evangelos E. Papalexakis,et al.  Automatic Unsupervised Tensor Mining with Quality Assessment , 2015, SDM.

[31]  Hui Chen,et al.  A literature survey on smart cities , 2015, Science China Information Sciences.

[32]  Ciro Cattuto,et al.  Anomaly Detection in Temporal Graph Data: An Iterative Tensor Decomposition and Masking Approach , 2015, AALTD@PKDD/ECML.

[33]  Danai Koutra,et al.  TimeCrunch: Interpretable Dynamic Graph Summarization , 2015, KDD.

[34]  Alain Barrat,et al.  Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys , 2015, PloS one.

[35]  Steve Harenberg,et al.  Anomaly detection in dynamic networks: a survey , 2015 .

[36]  Christos Faloutsos,et al.  Fast efficient and scalable Core Consistency Diagnostic for the parafac decomposition for big sparse tensors , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[37]  Christos Faloutsos,et al.  HaTen2: Billion-scale tensor decompositions , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[38]  Hong Chen,et al.  GPUTENSOR: Efficient tensor factorization for context-aware recommendations , 2015, Inf. Sci..

[39]  Julie Fournet,et al.  Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers , 2014, Network Science.

[40]  Lei Shi,et al.  STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery , 2015, Knowledge and Information Systems.

[41]  Peng Wang,et al.  Link prediction in social networks: the state-of-the-art , 2014, Science China Information Sciences.

[42]  Danai Koutra,et al.  Graph based anomaly detection and description: a survey , 2014, Data Mining and Knowledge Discovery.

[43]  J. H. Choi,et al.  DFacTo: Distributed Factorization of Tensors , 2014, NIPS.

[44]  Ananthram Swami,et al.  Com2: Fast Automatic Discovery of Temporal ('Comet') Communities , 2014, PAKDD.

[45]  Christos Faloutsos,et al.  Spotting misbehaviors in location-based social networks using tensors , 2014, WWW.

[46]  Pasquale De Meo,et al.  Detecting criminal organizations in mobile phone networks , 2014, Expert Syst. Appl..

[47]  Donald Goldfarb,et al.  Robust Low-Rank Tensor Recovery: Models and Algorithms , 2013, SIAM J. Matrix Anal. Appl..

[48]  Ciro Cattuto,et al.  Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach , 2013, PloS one.

[49]  Leman Akoglu,et al.  An Ensemble Approach for Event Detection and Characterization in Dynamic Graphs , 2014 .

[50]  Christos Faloutsos,et al.  FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop , 2014, SDM.

[51]  Pauli Miettinen,et al.  Discovering facts with boolean tensor tucker decomposition , 2013, CIKM.

[52]  A. Barrat,et al.  Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors , 2013, PloS one.

[53]  George Michailidis,et al.  Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[54]  Guangdong Feng,et al.  Traffic volume data outlier recovery via tensor model , 2013 .

[55]  Guangdong Feng,et al.  A Tensor Based Method for Missing Traffic Data Completion , 2013 .

[56]  Nikos D. Sidiropoulos,et al.  From K-Means to Higher-Way Co-Clustering: Multilinear Decomposition With Sparse Latent Factors , 2013, IEEE Transactions on Signal Processing.

[57]  Rizal Setya Perdana.  What is Twitter , 2013 .

[58]  Jure Leskovec,et al.  Learning to Discover Social Circles in Ego Networks , 2012, NIPS.

[59]  F. Jordán,et al.  Studying protein-protein interaction networks: a systems view on diseases. , 2012, Briefings in functional genomics.

[60]  Danai Koutra,et al.  TensorSplat: Spotting Latent Anomalies in Time , 2012, 2012 16th Panhellenic Conference on Informatics.

[61]  Nikos D. Sidiropoulos,et al.  ParCube: Sparse Parallelizable Tensor Decompositions , 2012, ECML/PKDD.

[62]  Christos Faloutsos,et al.  Fast mining and forecasting of complex time-stamped events , 2012, KDD.

[63]  Christos Faloutsos,et al.  GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries , 2012, KDD.

[64]  Sudipto Guha,et al.  Graph sketches: sparsification, spanners, and subgraphs , 2012, PODS.

[65]  Joseph T. Lizier,et al.  Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: A walk counting approach , 2012, 1203.0502.

[66]  Tamara G. Kolda,et al.  On Tensors, Sparsity, and Nonnegative Factorizations , 2011, SIAM J. Matrix Anal. Appl..

[67]  Stanford,et al.  Learning to Discover Social Circles in Ego Networks , 2012 .

[68]  A. Lo,et al.  Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors , 2011 .

[69]  João Gama,et al.  Visualizing the Evolution of Social Networks , 2011, EPIA.

[70]  Christos Faloutsos,et al.  MultiAspectForensics: Pattern Mining on Large-Scale Heterogeneous Networks with Tensor Analysis , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[71]  Przemyslaw Kazienko,et al.  Matching Organizational Structure and Social Network Extracted from Email Communication , 2011, BIS.

[72]  Sahin Albayrak,et al.  Link Prediction on Evolving Data Using Tensor Factorization , 2011, PAKDD Workshops.

[73]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[74]  Tamara G. Kolda,et al.  All-at-once Optimization for Coupled Matrix and Tensor Factorizations , 2011, ArXiv.

[75]  Andrzej Cichocki,et al.  PARAFAC algorithms for large-scale problems , 2011, Neurocomputing.

[76]  Ciro Cattuto,et al.  What's in a crowd? Analysis of face-to-face behavioral networks , 2010, Journal of theoretical biology.

[77]  Tamara G. Kolda,et al.  Temporal Link Prediction Using Matrix and Tensor Factorizations , 2010, TKDD.

[78]  Reinhard Schneider,et al.  Using graph theory to analyze biological networks , 2011, BioData Mining.

[79]  Wei Peng,et al.  Temporal relation co-clustering on directional social network and author-topic evolution , 2011, Knowledge and Information Systems.

[80]  Piero Fariselli,et al.  Blurring contact maps of thousands of proteins: what we can learn by reconstructing 3D structure , 2011, BioData Mining.

[81]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[82]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[83]  Evimaria Terzi,et al.  GraSS: Graph Structure Summarization , 2010, SDM.

[84]  Jimeng Sun,et al.  MultiVis: Content-Based Social Network Exploration through Multi-way Visual Analysis , 2009, SDM.

[85]  Sewoong Oh,et al.  A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion , 2009, ArXiv.

[86]  Jieping Ye,et al.  Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[87]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[88]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[89]  L. K. Hansen,et al.  Automatic relevance determination for multi‐way models , 2009 .

[90]  Jimeng Sun,et al.  MetaFac: community discovery via relational hypergraph factorization , 2009, KDD.

[91]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[92]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[93]  Tamara G. Kolda,et al.  Scalable Tensor Decompositions for Multi-aspect Data Mining , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[94]  Philip S. Yu,et al.  Incremental tensor analysis: Theory and applications , 2008, TKDD.

[95]  Lars Kai Hansen,et al.  Algorithms for Sparse Nonnegative Tucker Decompositions , 2008, Neural Computation.

[96]  Jimeng Sun,et al.  Two heads better than one: pattern discovery in time-evolving multi-aspect data , 2008, Data Mining and Knowledge Discovery.

[97]  Hsinchun Chen,et al.  Uncovering the dark Web: A case study of Jihad on the Web , 2008, J. Assoc. Inf. Sci. Technol..

[98]  Steffen Staab,et al.  PINTS: peer-to-peer infrastructure for tagging systems , 2008, IPTPS.

[99]  Pablo Villoslada,et al.  A computational analysis of protein-protein interaction networks in neurodegenerative diseases , 2008, BMC Systems Biology.

[100]  Tamara G. Kolda,et al.  Efficient MATLAB Computations with Sparse and Factored Tensors , 2007, SIAM J. Sci. Comput..

[101]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[102]  Tamara G. Kolda,et al.  Temporal Analysis of Semantic Graphs Using ASALSAN , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[103]  P. Grünwald.  The Minimum Description Length Principle (Adaptive Computation and Machine Learning) , 2007 .

[104]  Michael W. Mahoney,et al.  A randomized algorithm for a tensor-based generalization of the singular value decomposition , 2007 .

[105]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[106]  Jimeng Sun,et al.  Beyond streams and graphs: dynamic tensor analysis , 2006, KDD '06.

[107]  Tamara G. Kolda,et al.  MATLAB Tensor Toolbox , 2006 .

[108]  H. Kiers,et al.  Selecting among three-mode principal component models of different types and complexities: a numerical convex hull based method. , 2006, The British journal of mathematical and statistical psychology.

[109]  David J. Marchette,et al.  Scan Statistics on Enron Graphs , 2005, Comput. Math. Organ. Theory.

[110]  M. Keeling,et al.  Networks and epidemic models , 2005, Journal of The Royal Society Interface.

[111]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[112]  Tamir Hazan,et al.  Non-negative tensor factorization with applications to statistics and computer vision , 2005, ICML.

[113]  Hsinchun Chen,et al.  Criminal network analysis and visualization , 2005, CACM.

[114]  Sebastiano Vigna,et al.  The webgraph framework I: compression techniques , 2004, WWW '04.

[115]  R. Bro,et al.  A new efficient method for determining the number of components in PARAFAC models , 2003 .

[116]  Henk A L Kiers,et al.  A fast method for choosing the numbers of components in Tucker3 analysis. , 2003, The British journal of mathematical and statistical psychology.

[117]  Michael Ley,et al.  The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives , 2002, SPIRE.

[118]  Rasmus Bro,et al.  The N-way Toolbox for MATLAB , 2000 .

[119]  H. Kiers.  Towards a standardized notation and terminology in multiway analysis , 2000 .

[120]  H. Kiers,et al.  Three-mode principal components analysis: choosing the numbers of components and sensitivity to local optima. , 2000, The British journal of mathematical and statistical psychology.

[121]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[122]  FaloutsosMichalis,et al.  On power-law relationships of the Internet topology , 1999 .

[123]  Michalis Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.

[124]  R. Bro,et al.  A fast non‐negativity‐constrained least squares algorithm , 1997 .

[125]  M. Shubik,et al.  Price Variations in a Stock Market with Many Agents , 1996, cond-mat/9609144.

[126]  H. Kiers.  An alternating least squares algorithms for PARAFAC2 and three-way DEDICOM , 1993 .

[127]  J. Chang,et al.  Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .

[128]  Richard A. Harshman,et al.  Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .

[129]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[130]  L. Tucker,et al.  Some mathematical notes on three-mode factor analysis , 1966, Psychometrika.

[131]  F. L. Hitchcock.  The Expression of a Tensor or a Polyadic as a Sum of Products , 1927 .