Abstract
The size of networks now needed to model real world phenomena poses significant computational challenges. Key node selection in networks, (KNSIN) presented in this paper, selects a representative set of nodes that preserves the sketch of original nodes in the network and thus, serves as a useful solution to this challenge. KNSIN is accomplished via a sparse coding algorithm that efficiently learns a basis set over the feature space defined by the nodes. By executing a stop criterion, KNSIN automatically learns the dimensionality of the node space and guarantees that the learned basis accurately preserves the sketch of the original node space. In experiments, we use two large scale network datasets to evaluate the proposed KNSIN framework. Our results on the two datasets demonstrate the effectiveness of the KNSIN algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Allen, F., Babus, A.: Networks in finance. In: Network-based Strategies and Competencies, pp. 367–382 (2009)
Bhuiyan, M.: Guise: Uniform sampling of graphlets for large graph analysis. In: ICDM 2012 (2012)
Bishop, C.: Pattern Recognition and Machine Learning. Springer (2006)
Bonnans, J., Gilbert, J., Lemarechal, C., Sagastizabal, C.: Numerical optimization: theoretical and practical aspects. Springer-Verlag New York Inc. (2006)
Chen, B.-C., Kuo, Y.-H., Chen, Y.-Y., Chu, K.-Y., Hsu, W.: Semi-supervised face image retrieval using sparse coding with identity constraint. In: ACM MM 2011, pp. 1369–1372 (2011)
Chen, Z., et al.: Inferring social contextual behavior from bluetooth traces. In: UbiComp 2013 (2013)
Chen, Z., Zhou, J., Chen, Y., Chen, X., Gao, X.: Deploying a social community network in rural areas based on wireless mesh networks. In: IEEE YC-ICT 2009 (2009)
Dimitropoulos, X.A., Riley, G.F.: Creating realistic BGP models. In: IEEE/ACM MASCOTS 2003 (2003)
Dong, W., Charikar, M., Li, K.: Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces. In: SIGIR 2008, pp. 479–490 (2008)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Annals of Statistics 32(2), 407–419 (2004)
Gao, H., Tang, J., Hu, X., Liu, H.: Modeling temporal effects of human mobile behavior on location-based social networks. In: ACM CIKM (2013)
Gao, H., Tang, J., Liu, H.: Exploring social-historical ties on location-based social networks. In: AAAI (2012)
Gao, X., et al.: SOML: Sparse online metric learning with application to image retrieval. In: AAAI 2014 (2014)
Golub, G.H., Loan, C.F.V.: Matrix Computations, 3rd edn. Johns Hopkins University Press (1996)
Hasan, M.A., Zaki, M.J.: Output space sampling for graph patterns. In: VLDB 2009 (2009)
Kadushin, C.: Understanding social networks: Theories, concepts, and findings. Oxford University Press (2012)
Lakkaraju, H., McAuley, J., Leskovec, J.: What’s in a name? Understanding the interplay between titles, content, and communities in social media. In: AAAI 2013 (2013)
Lee, H., Battle, A., Rajat, R., Ng, A.Y.: Efficient sparse coding algorithms. In: NIPS 2007 (2007)
Leordeanu, M., Hebert, M., Sukthankar, R.: Beyond local appearance: Category recognition from pairwise interactions of simple features. In: CVPR 2007 (2007)
Leskovec, J., Faloutsos, C.: Sampling from large graphs. In: SIGKDD 2006 (2006)
Lewicki, M.S., Sejnowski, T.J.: Learning overcomplete representations. Neural Computation 12(2), 337–365 (2000)
Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: CIKM 2003 (2003)
Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In: SIGKDD 2010 (2010)
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: ICCV 2009 (2009)
Mallat, S., Zhang, Z.: Matching pursuit in a time-frequency dictionary. IEEE Trans. Signal Processing 41(12), 3397–3415 (1993)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology 27(1), 415–444 (2001)
Miller, K.T.: Bayesian nonparametric latent feature models. Ph.D. Thesis, University of California, Berkeley (2011)
Neville, J., Jensen, D., Friedland, L., Hay, M.: Learning relational probability trees. In: SIGKDD 2003 (2003)
Newman, M.: The structure and function of complex networks. SIAM Review 45(1), 167–256 (2003)
Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research 37, 3311–3325 (1997)
Osborne, M.R., Presnell, B., Turlach, B.A.: On the lASSO and its dual. Journal of Computational and Graphical Statistics 9(2), 319–337 (2000)
Proakis, J.G.: Digital Signal Processing: Principles, Algorithms and Applications. Prentice-Hall Press (2000)
Protter, M., Elad, M.: Image sequence denoising via sparse and redundant representations. IEEE Trans. Image Processing 18(1), 27–36 (2009)
Raydan, M.: On the barzilai and borwein choice of the step length for the gradient method. IMA Journal on Numerical Analysis 13, 321–326 (1993)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Sallee, P., Olshausen, B.A.: Learning sparse multiscale image representations. In: NIPS 2002 (2002)
Shervashidzea, N.: Efficient graphlet kernels for large graph comparison. In: AISTATS 2009 (2009)
Sporns, O.: Network analysis, complexity, and brain function. Complexity 8(1), 56–60 (2002)
Tang, L., Liu, H.: Relational learning via latent social dimensions. In: SIGKDD 2009 (2009)
Uetz, P., et al.: A comprehensive analysis of protein-protein interactions in saccharomyces cerevisiae. Nature 403, 623–627 (2000)
Weisberg, S.: Applied Linear Regression. Wiley, New York (1980)
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. PAMI 31(2), 210–227 (2009)
Xiang, S., Shen, X., Ye, J.: Efficient sparse group feature selection via nonconvex optimization. In: ICML 2013 (2013)
Xu, Y.: Orthogonal component analysis for dimensionality reduction, Nanjing Univerisity (2010)
Xu, Y., Rockmore, D.: Feature selection for link prediction. In: PIKM 2012 (2012)
Xu, Y., Rockmore, D., Kleinbaum, A.M.: Hyperlink prediction in hypernetworks using latent social features. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds.) DS 2013. LNCS, vol. 8140, pp. 324–339. Springer, Heidelberg (2013)
Xu, Y., Shen, F., Ping, W., Zhao, J.: Takes: a fast method to select features in the kernel space. In: CIKM 2011 (2011)
Zhu, J.: Max-margin nonparametric latent feature models for link prediction. In: ICML 2012 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, Y., Rockmore, D. (2014). Sparse Coding for Key Node Selection over Networks. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds) Discovery Science. DS 2014. Lecture Notes in Computer Science(), vol 8777. Springer, Cham. https://doi.org/10.1007/978-3-319-11812-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-11812-3_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11811-6
Online ISBN: 978-3-319-11812-3
eBook Packages: Computer ScienceComputer Science (R0)