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» Generic Graph Algorithms for Sparse Matrix Ordering
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CORR
2011
Springer
199views Education» more  CORR 2011»
13 years 3 months ago
Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining
Scaling up the sparse matrix-vector multiplication kernel on modern Graphics Processing Units (GPU) has been at the heart of numerous studies in both academia and industry. In thi...
Xintian Yang, Srinivasan Parthasarathy, Ponnuswamy...
CP
2003
Springer
14 years 1 months ago
A SAT-Based Approach to Multiple Sequence Alignment
Multiple sequence alignment is a central problem in Bioinformatics. A known integer programming approach is to apply branch-and-cut to exponentially large graph-theoretic models. T...
Steven David Prestwich, Desmond G. Higgins, Orla O...
SDM
2007
SIAM
143views Data Mining» more  SDM 2007»
13 years 10 months ago
Less is More: Compact Matrix Decomposition for Large Sparse Graphs
Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, resea...
Jimeng Sun, Yinglian Xie, Hui Zhang, Christos Falo...
JCP
2008
171views more  JCP 2008»
13 years 8 months ago
Mining Frequent Subgraph by Incidence Matrix Normalization
Existing frequent subgraph mining algorithms can operate efficiently on graphs that are sparse, have vertices with low and bounded degrees, and contain welllabeled vertices and edg...
Jia Wu, Ling Chen
ICCS
2005
Springer
14 years 2 months ago
Vectorized Sparse Matrix Multiply for Compressed Row Storage Format
Abstract. The innovation of this work is a simple vectorizable algorithm for performing sparse matrix vector multiply in compressed sparse row (CSR) storage format. Unlike the vect...
Eduardo F. D'Azevedo, Mark R. Fahey, Richard Tran ...