The laplacian paradigm: Emerging algorithms for massive graphs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6108 LNCS, Issue , 2010, Pages 2-14

  Teng, Shang Hua ^a  

^a University of Southern California ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL PROBLEM; DATA ANALYSIS; EFFICIENT ALGORITHM; EIGEN-VALUE; FINITE ELEMENT SYSTEM; FLOW COMPUTATION; FUNDAMENTAL PROBLEM; IMAGE PROCESS; LAPLACIAN MATRICES; LAPLACIANS; LINEAR TIME; LINEAR-ALGEBRAIC; LINEAR-TIME ALGORITHMS; MACHINE-LEARNING; MASSIVE GRAPH; MATCHINGS; N-VERTEX GRAPH; OTHER APPLICATIONS; RANDOM SAMPLING; SCIENTIFIC COMPUTING; SEMI-SUPERVISED LEARNING; SOCIAL NETWORK ANALYSIS; SPAM DETECTION; SPANNING TREE; SPECTRAL APPROXIMATIONS; SPECTRAL GRAPH THEORY;

CLUSTERING ALGORITHMS; COMBINATORIAL OPTIMIZATION; DATA REDUCTION; EIGENVALUES AND EIGENFUNCTIONS; ELECTRIC NETWORK ANALYSIS; GRAPH THEORY; IMAGE PROCESSING; INTERNET; LAPLACE TRANSFORMS; LEARNING ALGORITHMS; LINEAR SYSTEMS; PROBLEM SOLVING; SUPERVISED LEARNING;

APPROXIMATION ALGORITHMS;

EID: 77954447514     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13562-0_2     Document Type: Conference Paper

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