List of Papers

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    ================= Graphs: models ====================================================
     
  1. Mean-field theory for scale-free random networks
    by Albert-Laszlo Barabasi & Reka Albert & Hawoong Jeong
    Physica A: Statistical Mechanics and its Applications 272 pp. 173-187
    https://www.sciencedirect.com/science/article/pii/S0378437199002915?via%3Dihub
    1999
  2. From scale-free to Erdos-Renyi networks
    by Jesus Gomez-Gardenes & Yamir Moreno
    Phys. Rev. E 73, 056124
    https://journals.aps.org/pre/abstract/10.1103/PhysRevE.73.056124
    2006

     
    ================= Graphs: laplacian, eigenvalues, centrality measures ====================================================
     
  3. Eigenvector-like measures of centrality for asymmetric relations
    by P Bonacich & P Lloyd
    Social networks
    https://www.sciencedirect.com/science/article/pii/S0378873301000387
    2001
  4. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
    by Mikhail Belkin & Partha Niyogi
    Neural Computation 15 pp. 1373-1396
    http://www2.imm.dtu.dk/projects/manifold/Papers/Laplacian.pdf;
    https://www.mitpressjournals.org/doi/pdf/10.1162/089976603321780317
    2003
    presented by Boley
  5. Commute Times for a Directed Graph using an Asymmetric Laplacian
    by Daniel Boley & Gyan Ranjan & Zhi-Li Zhang
    Linear Algebra and Appl 435 pp. 224-242
    DOI: DOI: 10.1016/j.laa.2011.01.030
    2011
  6. Incremental Computation of Pseudo-Inverse of Laplacian
    by Gyan Ranjan & Zhi-Li Zhang & Daniel L. Boley
    8th Annual International Conference on Combinatorial Optimization and Applications (COCOA 2014)
    Also in Combinatorial Optimization and Applications (Springer) pp 729-749.
    2014
  7. Cauchy Graph Embedding
    by Dijun Luo & Chris Ding & Feiping Nie & Heng Huang
    ICML
    https://icml.cc/2011/papers/353_icmlpaper.pdf
    2011

     
    ==== since 2015:
     
  8. On the Limiting Behavior of Parameter-Dependent Network Centrality Measures
    by Michele Benzi & Christine Klymko
    SIMAX 36 2 pp. 686-706
    https://epubs.siam.org/doi/abs/10.1137/130950550
    DOI: https://doi.org/10.1137/130950550
    2015
    presented by Kharkwal.
  9. Random walk centrality in interconnected multilayer networks
    by Albert Sole-Ribalta & Manlio De Domenico & Sergio Gomez & Alex Arenas
    Physica D: Nonlinear Phenomena 323-324 pp. 73-79
    https://www.sciencedirect.com/science/article/pii/S0167278916000026
    confusing notation.
    2016
  10. Maintaining the duality of closeness and betweenness centrality
    by Ulrik Brandes & Stephen P.Borgatti & Linton C.Freeman
    Social Networks 44 pp. 153-159
    https://www.sciencedirect.com/science/article/pii/S0378873315000738
    DOI: https://doi.org/10.1016/j.socnet.2015.08.003
    tensor of shortest paths.
    2016
  11. Asymmetric Transitivity Preserving Graph Embedding
    by Mingdong Ou & Peng Cui & Jian Pei & Ziwei Zhang & Wenwu Zhu
    22nd KDD pp. 1105-1114
    https://dl.acm.org/citation.cfm?id=2939751
    2016
  12. Using Centrality Measures to Predict Helpfulness-Based Reputation in Trust Networks
    by Pasquale De Meo & Katarzyna Musial-Gabrys & Domenico Rosaci & Giuseppe M. L. Sarne & Lora Aroyo
    ACM Transactions on Internet Technology (TOIT) - Special Issue on Affect and Interaction in Agent-based Systems and Social Media 17 1
    https://dl.acm.org/citation.cfm?id=2981545
    lacks details.
    2017
  13. Graph Embedding Techniques, Applications, and Performance: A Survey
    by Palash Goyal & Emilio Ferrara
    Knowledge-Based Systems 151 pp. 78-94
    https://arxiv.org/abs/1705.02801
    https://github.com/palash1992/GEM (software).
    2018
  14. Markov fundamental tensor and its applications to network analysis
    by Golshan Golnari & Zhi-Li Zhang & Daniel Boley
    Linear Algebra and Appl 564 pp. 126-158
    http://www.sciencedirect.com/science/article/pii/S0024379518305470
    2019

     
    ================== CNNs =====================================
     
  15. Gradient Based Learning Applied to Document Recognition
    by Yann LeCun & Leon Bottou & Yoshua Bengio & Patrick Haffner
    Proc of the IEEE
    http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
  16. Convolutional Networks and Applications in Vision
    by Yann LeCun & Koray Kavukvuoglu & Clement Farabet
    Proc. International Symposium on Circuits and Systems (ISCAS'10) (IEEE)
    http://yann.lecun.com/exdb/publis/pdf/lecun-iscas-10.pdf
    2010
    presented by Kayala.
  17. Deep Convolutional Networks on Graph-Structured Data
    by Mikael Henaff & Joan Bruna & Yann LeCun
    https://arxiv.org/abs/1506.05163
    2015

     
    ================== convergence deep learning ==============================================
     
  18. Deep learning via hessian-free optimization
    by J Martens
    ICML
    http://www.cs.toronto.edu/~asamir/cifar/HFO_James.pdf [SLIDES]
    [SLIDES].
    2010
  19. Understanding the difficulty of training deep feedforward neural networks
    by X Glorot & Y Bengio
    Proceedings 13th Intl Conf AISTATS
    http://www.jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf?hc_location=ufi
    2010
    presented by Huang.
  20. Deep Sparse Rectifier Neural Networks
    by Xavier Glorot & Antoine Bordes & Yoshua Bengio
    14th AISTATS; JMLR vol 15
    2011
  21. On the importance of initialization and momentum in deep learning
    by I Sutskever & J Martens & G Dahl & G Hinton
    ICML
    http://www.jmlr.org/proceedings/papers/v28/sutskever13.pdf
    2013
  22. Spectral networks and deep locally connected networks on graphs
    by Joan Bruna & Wojciech Zaremba & Arthur Szlam & and Yann LeCun
    Proceedings of the 2nd International Conference on Learning Representations
    https://arxiv.org/abs/1312.6203
    2013
  23. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
    by Alec Radford & Luke Metz & Soumith Chintala
    https://arxiv.org/abs/1511.06434
    2015
    presented by Yang.
  24. Deep learning with limited numerical precision
    by S Gupta & A Agrawal & K Gopalakrishnan & P Narayanan
    32nd ICML
    http://www.jmlr.org/proceedings/papers/v37/gupta15.pdf;
    http://proceedings.mlr.press/v37/gupta15.html
    2015
  25. Semi-Supervised Classification with Graph Convolutional Networks
    by Thomas N. Kipf & Max Welling
    https://arxiv.org/abs/1609.02907
    2016
  26. Revisiting semi-supervised learning with graph embeddings
    by Z Yang & WW Cohen & R Salakhutdinov
    arXiv preprint arXiv:1603.08861
    https://arxiv.org/abs/1603.08861
    2016
  27. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
    by Defferrard, Micha\"{e}l & Bresson, Xavier & Vandergheynst, Pierre
    Advances in Neural Information Processing Systems 29 pp. 3844--3852
    http://papers.nips.cc/paper/6081-convolutional-neural-networks-on-graphs-with-fast-localized-spectral-filtering.pd
    2016
    presented by Houssou.
  28. On the Convergence of Adam and Beyond
    by Sashank J. Reddi & Satyen Kale & Sanjiv Kumar
    https://arxiv.org/abs/1904.09237
    2019

     
    ==== time series ==========================================
     
  29. A review of unsupervised feature learning and deep learning for Time Series Modeling
    by Martin Langkvist & Lars Karlsson & Amy Loutfi
    https://www.sciencedirect.com/science/article/pii/S0167865514000221;
    http://aass.oru.se/~mlt/review.pdf;
    http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.724.5069&rep=rep1&type=pdf
    presented by Unnikrishnan.

     
    ======= tensors in ML
     

     
    === Kolda
     
  30. Introduction to Tensor Decompositions and their Applications in Machine Learning
    by S Rabanser
    https://arxiv.org/pdf/1711.10781
    2017
    presented by Hoang.
  31. Tensor Decomposition for Signal Processing and Machine Learning
    by N Sidiropoulos & Lieven De Lathauwer & Xiao Fu & Kejun Huang & Evangelos E Papalexakis & Christos Faloutsos
    IEEE SP 65 12 pp. 3551-3582
    https://ieeexplore.ieee.org/abstract/document/7891546
    2017

     
    =========
     
  32. Unsupervised Learning of Video Representations using LSTMs
    by Nitish Srivastava & Elman Mansimov & Ruslan Salakhutdinov
    32nd ICML
    https://arxiv.org/abs/1502.04681
    2015
    presented by Kou.
  33. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
    by Xingjian SHI & Zhourong Chen & Hao Wang & Dit-Yan Yeung & Wai-kin Wong & Wang-chun WOO
    Advances in Neural Information Processing Systems 28 (NIPS 2015)
    http://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting
    2015
    presented by Kayala.

     
    ======= graph NNs
     
  34. Graph Neural Networks: A Review of Methods and Applications
    by Jie Zhou & Ganqu Cui & Zhengyan Zhang & Cheng Yang & Zhiyuan Liu & Lifeng Wang & Changcheng Li & Maosong Sun
    https://arxiv.org/abs/1812.08434
    2018

  35. by Steeve Huang
    https://towardsdatascience.com/a-gentle-introduction-to-graph-neural-network-basics-deepwalk-and-graphsage-db5d540d50b3
  36. The Graph Neural Network Model
    by Franco Scarselli & Marco Gori & Ah Chung Tsoi & Markus Hagenbuchner & Gabriele Monfa
    IEEE Transactions on Neural Net 20 1
    https://ieeexplore.ieee.org/abstract/document/4700287;
    https://persagen.com/files/misc/scarselli2009graph.pdf
    2008

     
    ==============================================================
     

  37. https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
  38. Face Recognition: A Convolutional Neural-Network Approach
    by Steve Lawrence & C. Lee Giles & Ah Chung Tsoi & Andrew D. Back
    IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 1, JANUARY 1997
    http://www.cs.cmu.edu/afs/cs/user/bhiksha/WWW/courses/deeplearning/Fall.2016/pdfs/Lawrence_et_al.pdf
    1997

     
    ==== dropout
     
  39. Regularization of Neural Networks using DropConnect
    by Li Wan & Matthew Zeiler & Sixin Zhang & Yann LeCun & Rob Fergus
    ICML 2013, JMLR W-CP vol 28
    http://proceedings.mlr.press/v28/wan13.pdf
    2013
    presented by Chen.

     
    ==== word2vec (like an autoencoder)
     
  40. Distributed Representations of Words and Phrases and their Compositionality
    by Tomas Mikolov & Ilya Sutskever & Kai Chen & Greg Corrado & Jeffrey Dean
    26th NIPS
    https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf;
    https://arxiv.org/abs/1310.4546
    2013
    presented by Dagvadorj.

     
    ==== node2vec
     
  41. node2vec: Scalable Feature Learning for Networks
    by Aditya Grover & Jure Leskovec
    KDD
    https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf
    2016
    presented by Ni.

     
    ==== GNN
     

  42. https://towardsdatascience.com/a-gentle-introduction-to-graph-neural-network-basics-deepwalk-and-graphsage-db5d540d50b3
  43. Graph Neural Networks: A Review of Methods and Applications
    by Jie Zhou & Ganqu Cui & Zhengyan Zhang & Cheng Yang & Zhiyuan Liu & Lifeng Wang & Changcheng Li & Maosong Sun
    https://arxiv.org/abs/1812.08434
    2019
  44. Adversarial Attacks on Neural Networks for Graph Data
    by Daniel Zugner & Amir Akbarnejad & Stephan Gunnemann
    SIGKDD'18
    http://github.com/danielzuegner/nettack;
    https://www.kdd.in.tum.de/nettack
    2018

     
    ==== graph convolutional neural networks:
     
  45. Variational graph auto-encoders
    by Thomas Kipf & Max Welling
    NIPS
    https://arxiv.org/abs/1611.07308
    2016
  46. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
    by Michael Defferrard & Xavier Bresson & Pierre Vandergheynst
    NIPS
    http://papers.nips.cc/paper/6081-convolutional-neural-networks-on-graphs-with-fast-localized-spectral-filtering
    2016
  47. Graph embedding techniques, applications and perforamnce, a survey
    by Palash Goyal & Emilio Ferrara
    Knowledge-based System 151:78094
    https://arxiv.org/abs/1705.02801;
    https://www.sciencedirect.com/science/article/abs/pii/S0950705118301540
    2018

     
    === GNets based on message passing
     

     
    === Battaglia et al 2018
     
  48. Extrapolating paths with graph neural networks
    by Jean-Baptiste Cordonnier & Andreas Loukas
    IJCAI
    https://arxiv.org/abs/1903.07518
    2019

     
    ======= Bayes Opt
     
  49. A Tutorial on Bayesian Optimization
    by Peter I. Frazier
    https://arxiv.org/abs/1807.02811
    2018
  50. High Dimensional Bayesian Optimization Using Dropout
    by Cheng Li & Sunil Gupta & Santu Rana & Vu Nguyen & Svetha Venkatesh & Alistair Shilton
    IJCAI
    https://arxiv.org/abs/1802.05400
    2017

     
    ================ trust in networks? =====================================
     
  51. Exploring Different Types of Trust Propagation
    by Audun Josang & Stephen Marsh & Simon Pope
    Stolen K., Winsborough W.H., Martinelli F., Massacci F. (eds) Trust Management. iTrust 2006. Lecture Notes in Computer Science, vol 3986. Springer, Berlin, Heidelberg
    https://link.springer.com/chapter/10.1007/11755593_14
    DOI: https://doi.org/10.1007/11755593-14
    2006
  52. A matrix factorization technique with trust propagation for Recommendation in Social Networks
    by M Jamali
    http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.459.691&rep=rep1&type=pdf
    2010
    to be presented by Unnikrishnan.
  53. Propagation of Trust and Distrust
    by R Guha
    www.shibbo.ethz.ch/CDstore/www2004/docs/1p403.pdf
    2004
  54. Trust propagation through both explicit and implicit social networks
    patent US8442978B2
  55. Trust-aware recommender systems
    by Massa & Paolo & Avesani & Paolo
    Proceedings of the 2007 ACM Conference on Recommender Systems, RecSys '07 pp. 17--24
    http://doi.acm.org/10.1145/1297231.1297235;
    https://dl.acm.org/citation.cfm?id=1297235
    DOI: 10.1145/1297231.1297235
    2007
  56. Trust in Recommender Systems
    by John O'Donovan & Barry Smyth
    IUI '05 Proceedings of the 10th international conference on Intelligent user interfaces pp. 167-174
    https://dl.acm.org/citation.cfm?id=1040870
    2005
  57. Designing Trust Propagation Algorithms based on Simple Multiplicative Strategy for Social Networks
    by Partha SarathiChakrabortyaSunilKarformb
    Procedia Technology 6 pp. 534-539
    https://www.sciencedirect.com/science/article/pii/S2212017312006093
    2012
  58. A review on trust propagation and opinion dynamics in social networks and group decision making frameworks
    by Urena, Raquel & Kou, Gang & Dong, Yucheng & Chiclana, Francisco & Herrera-Viedma, Enrique
    Information Sciences 478 pp. 461-475
    http://www.sciencedirect.com/science/article/pii/S0020025518309253
    DOI: https://doi.org/10.1016/j.ins.2018.11.037
    2019
  59. Trust Propagation with Mixed-Effects Models
    by Overgoor, Jan & Wulczyn, Ellery & Potts, Christopher
    ICWSM
    2012
  60. New Concepts for Trust Propagation in Knowledge Processing Systems
    by Markus Jager & Josef Kung
    Institute for Application Oriented Knowledge Processing
    ceur-ws.org/Vol-2154/paper1.pdf;
    https://ieeexplore.ieee.org/abstract/document/8456081/
    2018
  61. Research on Trust Propagation Models in Reputation Management Systems
    by Su, Zhiyuan & Li, Mingchu & Fan, Xinxin & Jin, Xing & Wang, Zhen
    Mathematical Problems in Engineering 2014 pp. 16
    https://www.hindawi.com/journals/mpe/2014/536717/
    DOI: http://dx.doi.org/10.1155/2014/536717
    2014

    Additional Papers

  62. RecWalk: Nearly Uncoupled Random Walks for Top-N Recommendation
    by Athanasios N. Nikolakopoulos & George Karypis
    Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining pp. 150-158
    2019
    presented by Kalantzi.
  63. Tensor decompositions and applications
    by Kolda TG & Bader BW
    SIAM Rev. 51 pp. 455-500
    http://epubs.siam.org/doi/abs/10.1137/07070111X
    2009
    presented by Boley.
  64. Ensemble deep learning for regression and time series forecasting
    by Xueheng Qiu & Le Zhang & Ye Ren & P. N. Suganthan & Gehan Amaratunga
    IEEE Symp Computational Intelligence in Ensemble Learning (CIEL)
    https://ieeexplore.ieee.org/document/7015739
    2014
    presented by Houssou.
Back to class web site
  • Computing Kantorovich-Wasserstein Distances ond-dimensional histograms using (d+1)-partite graphs
    by Gennaro Auricchio & Stefano Gualandi & Marco Veneroni & Federico Bassetti
    arXiv preprint arXiv:1805.07416v2
    https://arxiv.org/abs/1805.07416
    2018
  • A Tutorial on Network Embeddings
    by Haochen Chen & Bryan Perozzi & Rami Al-Rfou & Steven Skiena
    arXiv preprint arXiv:1808.02590
    https://arxiv.org/abs/1808.02590
    2018 ,
  • Representation Learning on Graphs: Methods and Applications
    by William L. Hamilton & Rex Ying & Jure Leskovec
    arXiv preprint arXiv:1709.05584
    https://arxiv.org/abs/1709.05584
    2017 ,
  • Generative Adversarial Nets
    by Ian J. Goodfellow & Jean Pouget-Abadie & Mehdi Mirza & Bing Xu & David Warde-Farley & Sherjil Ozair & Aaron Courville & Yoshua Bengio
    NIPS 2014
    http://papers.nips.cc/paper/5423-generative-adversarial-nets
    2014 ,
    presented by Chen.
  • Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
    by Chantat Eksombatchai & Pranav Jindal & Jerry Zitao Liu & Yuchen Liu & Rahul Sharma & Charles Sugnet & Mark Ulrich & Jure Leskovec
    WWW '18 pp. 1775-178
    https://dl.acm.org/citation.cfm?id=3186183
    2018
    presented by Kalantzi.
  • SinGAN: Learning a Generative Model from a Single Natural Image
    by Tamar Rott Shaham & Tomer Michaeli & Tali Dekel
    ICCV
    http://openaccess.thecvf.com/content_ICCV_2019/papers/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.pdf
    https://arxiv.org/abs/1905.01164
    http://webee.technion.ac.il/people/tomermic/SinGAN/SinGAN.htm
    2019
    presented by Kou.