power-law degree distribution / k-means algorithm / spectral clustering algorithm / spectral clustering algorithms / canonical spectral clustering algorithm / neural information processing systems / empirical networks / Social networks / sparse empirical networks / spectral algorithms / empirical network / above algorithm / large sparse networks / graph algorithms / power law distribution / sparse network / spectral algorithm / /
Organization
Stochastic Blockmodel SBM / KARL ROHE DEPARTMENT OF STATISTICS / National Institute of Health / National Science Foundation / American Statistical Association / WARF / /
Person
Sara Fernandes-Taylor / Frank McSherry / Kamal M. Ali / Elizaveta Levina / Peter J Bickel / Aiyou Chen / Brian Karrer / Mary Radcliffe / Rong K Chung / Michael I Jordan / Amin Coja-Oghlan / Linyuan Lu / John E Hopcroft / Michael W Mahoney / Andrew Y Ng / KARL ROHE / Anirban Dasgupta / Stephen A Vavasis / Arash A Amini / Jeffrey D. Scargle / Ashok N. Srivastava / Natalie Glance / Lada A Adamic / Peter H Sch / /
PoliticalEvent
presidential election / /
Product
Uzi / /
ProvinceOrState
South Carolina / /
PublishedMedium
Machine Learning / Journal of Machine Learning Research / Journal of the American Statistical Association / /
REGULARIZED SPECTRAL CLUSTERING UNDER THE DEGREE-CORRECTED STOCHASTIC BLOCKMODEL TAI QIN, KARL ROHE DEPARTMENT OF STATISTICS, UW-MADISON Abstract. Spectral clustering is a fast and popular algorithm for finding clusters