R. P. Agaev and P. Y. Chebotarev, Spanning forests of a digraph and their applications, Automation and Remote Control, vol.62, issue.3, pp.443-466, 2001.
DOI : 10.1023/A:1002862312617

R. Andersen, F. Chung, L. , and K. , Local Graph Partitioning using PageRank Vectors, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06), pp.475-486, 2006.
DOI : 10.1109/FOCS.2006.44

K. Avrachenkov, V. Dobrynin, D. Nemirovsky, S. K. Pham, and E. Smirnova, Pagerank based clustering of hypertext document collections, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pp.873-874, 2008.
DOI : 10.1145/1390334.1390549
URL : https://hal.archives-ouvertes.fr/inria-00565355

K. Avrachenkov, P. Gonçalves, A. Mishenin, and M. Sokol, Generalized Optimization Framework for Graph-based Semi-supervised Learning, Proceedings of SIAM Conference on Data Mining, 2012.
DOI : 10.1137/1.9781611972825.83
URL : https://hal.archives-ouvertes.fr/inria-00633818

K. Avrachenkov, P. Gonçalves, and M. Sokol, On the Choice of Kernel and Labelled Data in Semi-supervised Learning Methods, Algorithms and Models for the Web Graph, pp.56-67, 2013.
DOI : 10.1007/978-3-319-03536-9_5
URL : https://hal.archives-ouvertes.fr/hal-00924132

K. Avrachenkov, V. Mazalov, and B. Tsynguev, Beta Current Flow Centrality for Weighted Networks, Proceedings of the 4th International Conference on Computational Social Networks, p.9197, 2015.
DOI : 10.1007/978-3-319-21786-4_19
URL : https://hal.archives-ouvertes.fr/hal-01258658

D. Blackwell, Discrete dynamic programming " . The Annals of Mathematical Statistics, pp.719-726, 1962.

J. Callut, K. Françoisse, M. Saerens, and P. Dupont, Semi-supervised Classification from Discriminative Random Walks, Machine Learning and Knowledge Discovery in Databases European Conference Proceedings, Part I, ser, pp.162-177, 2008.
DOI : 10.1007/978-3-540-87479-9_29
URL : http://www.info.ucl.ac.be/~pdupont/pdupont/pdf/dwalks_ecml08.pdf

O. Chapelle, B. Schölkopf, and A. Zien, Semi-supervised learning, 2006.
DOI : 10.7551/mitpress/9780262033589.001.0001

P. Y. Chebotarev, Aggregation of preferences by the generalized row sum method, Mathematical Social Sciences, vol.27, issue.3, pp.293-320, 1994.
DOI : 10.1016/0165-4896(93)00740-L

P. Chebotarev, Spanning forests and the golden ratio, Discrete Applied Mathematics, vol.156, issue.5, pp.813-821, 2008.
DOI : 10.1016/j.dam.2007.08.030
URL : https://doi.org/10.1016/j.dam.2007.08.030

P. Chebotarev, The graph bottleneck identity, Advances in Applied Mathematics, vol.47, issue.3, pp.403-413, 2011.
DOI : 10.1016/j.aam.2010.11.001
URL : https://doi.org/10.1016/j.aam.2010.11.001

P. Chebotarev, . Yu, and E. V. Shamis, The matrix-forest theorem and measuring relations in small social groups " . Automation and Remote Control, pp.1505-1514, 1997.

P. Chebotarev, . Yu, and E. V. Shamis, On a duality between metrics and ?-proximities " . Automation and Remote Control, pp.608-612, 1998.

P. Chebotarev, . Yu, and E. V. Shamis, On proximity measures for graph vertices, Automation and Remote Control, vol.59, issue.10, pp.1443-1459, 1998.

P. Chebotarev, . Yu, and E. V. Shamis, The forest metrics of a graph and their properties, Automation and Remote Control, issue.8, pp.61-1364, 2000.

F. Chung and S. T. Yau, Coverings, heat kernels and spanning trees, Electronic Journal of Combinatorics, vol.6, p.12, 1999.

F. Chung and S. T. Yau, Discrete Green's Functions, Journal of Combinatorial Theory, Series A, vol.91, issue.1-2, pp.191-214, 2000.
DOI : 10.1006/jcta.2000.3094

F. Chung, The heat kernel as the pagerank of a graph, Proceedings of the National Academy of Sciences, vol.51, issue.50, pp.19735-19740, 2007.
DOI : 10.1002/rsa.3240040402

F. Chung, A local graph partitioning algorithm using heat kernel pagerank, Proceedings of WAW 2009, pp.62-75, 2009.
DOI : 10.1007/978-3-540-95995-3_6
URL : http://www.math.ucsd.edu/~fan/wp/kernel_local.pdf

F. Critchley, On certain linear mappings between inner-product and squared-distance matrices, Linear Algebra and its Applications, vol.105, pp.91-107, 1988.
DOI : 10.1016/0024-3795(88)90006-7
URL : https://doi.org/10.1016/0024-3795(88)90006-7

M. M. Deza and M. Laurent, Geometry of Cuts and Metrics, Algorithms and Combinatorics, vol.15, 1997.
DOI : 10.1007/978-3-642-04295-9
URL : http://www.liga.ens.fr/~deza/BOOK1/cutbook.pdf

F. Fouss, K. Francoisse, L. Yen, A. Pirotte, and M. Saerens, An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification, Neural Networks, vol.31, pp.31-53, 2012.
DOI : 10.1016/j.neunet.2012.03.001

A. V. Dorugade, New ridge parameters for ridge regression, Journal of the Association of Arab Universities for Basic and Applied Sciences, pp.94-99, 2014.
DOI : 10.1016/j.jaubas.2013.03.005
URL : https://doi.org/10.1016/j.jaubas.2013.03.005

F. Fouss, L. Yen, A. Pirotte, and M. Saerens, An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task, Sixth International Conference on Data Mining (ICDM'06), pp.863-868, 2006.
DOI : 10.1109/ICDM.2006.18

S. J. Kirkland, M. Neumann, and B. L. Shader, Distances in Weighted Trees and Group Inverse of Laplacian Matrices, SIAM Journal on Matrix Analysis and Applications, vol.18, issue.4, pp.827-841, 1997.
DOI : 10.1137/S0895479896298713

D. E. Knuth, The Stanford GraphBase: a platform for combinatorial computing, 1993.

R. I. Kondor and J. Lafferty, Diffusion kernels on graphs and other discrete input spaces, Proceedings of ICML, pp.315-322, 2002.

G. Muniz and B. M. Kibria, On Some Ridge Regression Estimators: An Empirical Comparisons, Communications in Statistics - Simulation and Computation, vol.32, issue.3, pp.621-630, 2009.
DOI : 10.1080/02664760500078946

M. E. Newman and M. Girvan, Finding and evaluating community structure in networks, Physical Review E, vol.65, issue.2, p.26113, 2004.
DOI : 10.1103/PhysRevE.68.065103

M. L. Puterman, Markov decision processes: Discrete stochastic dynamic programming, 1994.
DOI : 10.1002/9780470316887

H. Scheffé, An analysis of variance for paired comparisons, Journal of the American Statistical Association, vol.47, issue.259, pp.381-400, 1952.

A. J. Smola and R. I. Kondor, Kernels and Regularization on Graphs, Proceedings of the 16th Annual Conference on Learning Theory, pp.144-158, 2003.
DOI : 10.1007/978-3-540-45167-9_12
URL : http://mlg.anu.edu.au/~smola/./papers/SmoKon03.ps

L. Yen, M. Saerens, A. Mantrach, and M. Shimbo, A family of dissimilarity measures between nodes generalizing both the shortest-path and the commutetime distances, 14th ACM SIGKDD Intern. Conf. on Knowledge Discovery and Data Mining, pp.785-793, 2008.

D. Zhou and C. J. Burges, Spectral clustering and transductive learning with multiple views, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.1159-1166, 2007.
DOI : 10.1145/1273496.1273642
URL : http://imls.engr.oregonstate.edu/www/htdocs/proceedings/icml2007/papers/575.pdf

D. Zhou, O. Bousquet, N. Lal, T. Weston, J. Schölkopf et al., Learning with local and global consistency, Advances in Neural Information Processing Systems, pp.321-328, 2004.

X. Zhu, Z. Ghahramani, and J. Lafferty, Semi-supervised learning using Gaussian fields and harmonic functions, Proceedings of ICML 2003, pp.912-919, 2003.

X. Zhu, Semi-supervised learning literature survey, 2005.