Fetching the paper…
Reading the bibliography…
Spectral clustering is a technique that clusters elements using the top few eigenvectors of their (possibly normalized) similarity matrix.
Rohe, KarlK., Chatterjee, SouravS. andYu, BinB. (2011). Spectral clustering and the high-dimensional stochastic blockmodel. Ann. Statist. 39 1878–1915
1915
Earlier work this paper cites.
Katz, L.L. (1953). A new status index derived from sociometric analysis. Psychometrika 18 39–43
1953
Earlier work this paper cites.
Donath, W. E.W. E. andHoffman, A. J.A. J. (1973). Lower bounds for the partitioning of graphs. IBM J. Res. Develop. 17 420–425
1973
Earlier work this paper cites.
Fiedler, MiroslavM. (1973). Algebraic connectivity of graphs. Czechoslovak Math. J. 23 298–305
1973
Earlier work this paper cites.
Füredi, Z.Z. andKomlós, J.J. (1981). The eigenvalues of random symmetric matrices. Combinatorica 1 233–241
1981
Earlier work this paper cites.
Holland, P. W.P. W., Laskey, K. B.K. B. andLeinhardt, S.S. (1983). Stochastic blockmodels: First steps. Social Networks 5 109–137
1983
Earlier work this paper cites.
Pothen, AlexA., Simon, Horst D.H. D. andLiou, Kang-PuK.-P. (1990). Partitioning sparse matrices with eigenvectors of graphs. SIAM J. Matrix Anal. Appl. 11 430–452
1990
Earlier work this paper cites.
Hagen, Lars W.L. W. andKahng, Andrew B.A. B. (1992). New spectral methods for ratio cut partitioning and clustering. IEEE Trans. on CAD of Integrated Circuits and Systems 11 1074–1085
1992
Earlier work this paper cites.
Hendrickson, BruceB. andLeland, RobertR. (1995). An improved spectral graph partitioning algorithm for mapping parallel computations. SIAM J. Sci. Comput. 16 452–469
1995
Earlier work this paper cites.
Bollobás, BélaB. (1998). Modern Graph Theory. Graduate Texts in Mathematics 184. Springer, New York
1998
Cited alongside, same era.
Shi, JianboJ. andMalik, JitendraJ. (2000). Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22 888–905
2000
Cited alongside, same era.
Ng, Andrew Y.A. Y., Jordan, Michael I.M. I. andWeiss, YairY. (2001). On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada. MIT Press, Cambridge, MA
2001
Cited alongside, same era.
Liben-Nowell, D.D. andKleinberg, J.J. (2003). The link prediction problem for social networks. In Conference on Information and Knowledge Management ACM, New York
2003
Cited alongside, same era.
Adamic, Lada A.L. A. andGlance, NatalieN. (2005). The political blogosphere and the 2004 U.S. election: Divided they blog. In Proceedings of the 3rd Intl. Workshop on Link Discovery. ACM, New York
Oliveira, Roberto ImbuzeiroR. I. (2009). Concentration of the adjacency matrix and of the Laplacian in random graphs with independent edges. Preprint
2009
Later among the works it cites.
Chung, FanF. andRadcliffe, MaryM. (2011). On the spectra of general random graphs. Electron. J. Combin. 18 Paper 215, 14
2011
Later among the works it cites.
Chaudhuri, KamalikaK., Graham, Fan ChungF. C. andTsiatas, AlexanderA. (2012). Spectral clustering of graphs with general degrees in the extended planted partition model. Journal of Machine Learning Research—Proceedings Track 23 35.1–35.23
2012
Later among the works it cites.
Sussman, Daniel L.D. L., Tang, MinhM., Fishkind, Donniell E.D. E. andPriebe, Carey E.C. E. (2012). A consistent adjacency spectral embedding for stochastic blockmodel graphs. J. Amer. Statist. Assoc. 107 1119–1128
2012
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2004
Cited alongside, same era.
Feige, UrielU. andOfek, EranE. (2005). Spectral techniques applied to sparse random graphs. Random Structures Algorithms 27 251–275
2005
Cited alongside, same era.
von Luxburg, UlrikeU. (2007). A tutorial on spectral clustering. Stat. Comput. 17 395–416
2007
Cited alongside, same era.
2008
Cited alongside, same era.
Bickel, Peter J.P. J. andChen, AiyouA. (2009). A nonparametric view of network models and Newman Girvan and other modularities. Proc. Natl. Acad. Sci. USA 106 21068–21073
2009
Cited alongside, same era.
2013
Closest in time.
Qin, TaiT. andRohe, KarlK. (2013). Regularized spectral clustering under the degree-corrected stochastic blockmodel. In Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, USA. MIT Press, Cambridge, MA
2013
Closest in time.
Sarkar, P.P. andBickel, P. J.P. J. (2015). Supplement to “Role of normalization in spectral clustering for stochastic blockmodels.” DOI: \doiurl
2015
Closest in time.
Amini, Arash A.A. A., Chen, AiyouA., Bickel, Peter J.P. J. andLevina, ElizavetaE. (2013). Pseudo-likelihood methods for community detection in large sparse networks. Ann. Statist. 41 2097–2122
2097
Closest in time.