On Graph Mining With Deep Learning: Introducing Model R for Link Weight Prediction

Open access

Abstract

Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to the link weight prediction problem. This model uses a node embedding technique that extracts node embeddings (knowledge of nodes) from the known links’ weights (relations between nodes) and uses this knowledge to predict the unknown links’ weights. We demonstrate the power of Model R through experiments and compare it with the stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We analyze the node embeddings to confirm that closeness in embedding space correlates with stronger relationships as measured by the link weight. We anticipate this new approach will provide effective solutions to more graph mining tasks.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] A. Hannun C. Case J. Casper B. Catanzaro G. Diamos E. Elsen R. Prenger S. Satheesh S. Sengupta A. Coates et al Deep speech: Scaling up end-to-end speech recognition arXiv preprint arXiv:1412.5567 2014.

  • [2] K. Simonyan and A. Zisserman Very deep convolutional networks for large-scale image recognition arXiv preprint arXiv:1409.1556 2014.

  • [3] K. Yao G. Zweig M.-Y. Hwang Y. Shi and D. Yu Recurrent neural networks for language understanding. in INTERSPEECH 2013 pp. 2524–2528.

  • [4] O. Barkan and N. Koenigstein Item2vec: neural item embedding for collaborative filtering in Machine Learning for Signal Processing (MLSP) 2016 IEEE 26th International Workshop on. IEEE 2016 pp. 1–6.

  • [5] A. Grover and J. Leskovec node2vec: Scalable feature learning for networks in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM 2016 pp. 855–864.

  • [6] D. Liben-Nowell and J. Kleinberg The link-prediction problem for social networks Journal of the Association for Information Science and Technology vol. 58 no. 7 pp. 1019–1031 2007.

  • [7] M. Al Hasan V. Chaoji S. Salem and M. Zaki Link prediction using supervised learning in SDM06: Workshop on Link Analysis Counterterrorism and Security 2006.

  • [8] J. Zhao L. Miao J. Yang H. Fang Q.-M. Zhang M. Nie P. Holme and T. Zhou Prediction of links and weights in networks by reliable routes Scientific Reports vol. 5 2015.

  • [9] L. A. Adamic and E. Adar Friends and neighbors on the web Social Networks vol. 25 no. 3 pp. 211–230 2003.

  • [10] T. Murata and S. Moriyasu Link prediction of social networks based on weighted proximity measures in IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE 2007 pp. 85–88.

  • [11] H. A. Taha Operations Research: An Introduction (For VTU). Pearson Education India 1982.

  • [12] P. W. Holland K. B. Laskey and S. Leinhardt Stochastic blockmodels: First steps Social Networks vol. 5 no. 2 pp. 109–137 1983.

  • [13] C. Aicher A. Z. Jacobs and A. Clauset Learning latent block structure in weighted networks Journal of Complex Networks p. cnu026 2014.

  • [14] T. Mikolov I. Sutskever K. Chen G. S. Corrado and J. Dean Distributed representations of words and phrases and their compositionality in Advances in Neural Information Processing Systems 2013 pp. 3111–3119.

  • [15] T. Mikolov W.-t. Yih and G. Zweig Linguistic regularities in continuous space word representations. in NAACL HLT vol. 13 2013 pp. 746–751.

  • [16] Q. Le and T. Mikolov Distributed representations of sentences and documents in Proceedings of the 31st International Conference on Machine Learning (ICML-14) 2014 pp. 1188–1196.

  • [17] B. Perozzi R. Al-Rfou and S. Skiena Deepwalk: Online learning of social representations in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM 2014 pp. 701–710.

  • [18] P. Vincent H. Larochelle Y. Bengio and P.-A. Manzagol Extracting and composing robust features with denoising autoencoders in Proceedings of the 25th International Conference on Machine Learning. ACM 2008 pp. 1096–1103.

  • [19] G. E. Hinton and R. R. Salakhutdinov Reducing the dimensionality of data with neural networks Science vol. 313 no. 5786 pp. 504–507 2006.

  • [20] X. Feng Y. Zhang and J. Glass Speech feature denoising and dereverberation via deep autoencoders for noisy reverberant speech recognition in Acoustics Speech and Signal Processing (ICASSP) 2014 IEEE International Conference on. IEEE 2014 pp. 1759–1763.

  • [21] C.-Y. Liou W.-C. Cheng J.-W. Liou and D.-R. Liou Autoencoder for words Neurocomputing vol. 139 pp. 84–96 2014.

  • [22] O. Abdel-Hamid A.-r. Mohamed H. Jiang L. Deng G. Penn and D. Yu Convolutional neural networks for speech recognition IEEE/ACM Transactions on Audio Speech and Language Processing vol. 22 no. 10 pp. 1533–1545 2014.

  • [23] A. Van den Oord S. Dieleman and B. Schrauwen Deep content-based music recommendation in Advances in Neural Information Processing Systems 2013 pp. 2643–2651.

  • [24] A. Krizhevsky I. Sutskever and G. E. Hinton Imagenet classification with deep convolutional neural networks in Advances in Neural Information Processing Systems 2012 pp. 1097–1105.

  • [25] R. Collobert and J. Weston A unified architecture for natural language processing: Deep neural networks with multitask learning in Proceedings of the 25th International Conference on Machine Learning. ACM 2008 pp. 160–167.

  • [26] A. M. Elkahky Y. Song and X. He A multi-view deep learning approach for cross domain user modeling in recommendation systems in Proceedings of the 24th International Conference on World Wide Web. ACM 2015 pp. 278–288.

  • [27] R. Socher Y. Bengio and C. D. Manning Deep learning for nlp (without magic) in Tutorial Abstracts of ACL 2012. Association for Computational Linguistics 2012 pp. 5–5.

  • [28] R. Socher J. Bauer C. D. Manning and A. Y. Ng Parsing with compositional vector grammars. in ACL (1) 2013 pp. 455–465.

  • [29] R. Socher A. Perelygin J. Y.Wu J. Chuang C. D. Manning A. Y. Ng C. Potts et al. Recursive deep models for semantic compositionality over a sentiment treebank in Proceedings of the Conference on Empirical Methods on Natural Language Processing (EMNLP) vol. 1631 2013 p. 1642.

  • [30] Y. Shen X. He J. Gao L. Deng and G. Mesnil A latent semantic model with convolutional-pooling structure for information retrieval in Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. ACM 2014 pp. 101–110.

  • [31] D. E. Rumelhart G. E. Hinton and R. J. Williams Learning representations by back-propagating errors Cognitive Modeling vol. 5 no. 3 p. 1 1988.

  • [32] Y. A. LeCun L. Bottou G. B. Orr and K.-R. Müller Efficient backprop in Neural Networks: Tricks of the Trade. Springer 2012 pp. 9–48.

  • [33] J. Mairal F. Bach J. Ponce and G. Sapiro Online learning for matrix factorization and sparse coding The Journal of Machine Learning Research vol. 11 pp. 19–60 2010.

  • [34] S. Smale and D.-X. Zhou Learning theory estimates via integral operators and their approximations Constructive Approximation vol. 26 no. 2 pp. 153–172 2007.

  • [35] Y. LeCun Y. Bengio and G. Hinton Deep learning Nature vol. 521 no. 7553 pp. 436–444 2015.

  • [36] V. Colizza R. Pastor-Satorras and A. Vespignani Reaction–diffusion processes and metapopulation models in heterogeneous networks Nature Physics vol. 3 no. 4 pp. 276–282 2007.

  • [37] R. K. Pan K. Kaski and S. Fortunato World citation and collaboration networks: uncovering the role of geography in science Scientific Reports vol. 2 2012.

  • [38] M. A. Porter P. J. Mucha M. E. Newman and C. M. Warmbrand A network analysis of committees in the us house of representatives Proceedings of the National Academy of Sciences of the United States of America vol. 102 no. 20 pp. 7057–7062 2005.

  • [39] T. Opsahl and P. Panzarasa Clustering in weighted networks Social Networks vol. 31 no. 2 pp. 155–163 2009.

  • [40] M. Abadi P. Barham J. Chen Z. Chen A. Davis J. Dean M. Devin S. Ghemawat G. Irving M. Isard et al. Tensorflow: A system for large-scale machine learning. in Operating Systems Design and Implementation (OSDI) vol. 16 2016 pp. 265–283.

  • [41] F. M. Harper and J. A. Konstan The movielens datasets: History and context ACM Transactions on Interactive Intelligent Systems (TiiS) vol. 5 no. 4 p. 19 2015.

Search
Journal information
Impact Factor


CiteScore 2018: 4.70

SCImago Journal Rank (SJR) 2018: 0.351
Source Normalized Impact per Paper (SNIP) 2018: 4.066

Cited By
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 3301 900 38
PDF Downloads 887 438 47