Paper Info
Title
Efficient Graph Comparison and Visualization Using GPU
Abstract
This paper presents application of several graph algorithms for comparison and visualization of real-world networks. In order to obtain interactive and robust framework for analysis of large graphs we use CUDA implementations of all-shortest-paths (APSP) and breadth-first-search (BFS) algorithms along with CULA matrix decomposition routines. Such an approach allows for efficient computation of graph feature vectors, visualization with graph B-matrices and accelerating dimensionality reduction methods used to embed graphs into low-dimensional metric spaces. Graph analysis algorithms implemented in CUDA were integrated with Graph Investigator Java application via Java Native Interface (JNI) what makes them more convenient to use. We further present two real-world usage scenarios i.e. analysis and visualization of vascular networks in presence of tumor and clusterization based on graph representations of satelite photos.
Year
DOI
Venue
2011
10.1109/CSE.2011.100
C3S2E
Keywords
Field
DocType
graph representation,graph feature vector,efficient graph comparison,graph analysis,cuda implementation,graph b-matrices,java native interface,graph algorithm,graph investigator java application,large graph,embed graph,java,data visualisation,metric space,breadth first search,graph theory,matrix decomposition,shortest path,coprocessors,feature vector
Graph theory,Java Native Interface,Data visualization,Computer science,Visualization,CUDA,Power graph analysis,Theoretical computer science,Graph rewriting,Graph (abstract data type),Distributed computing
Conference
Citations 
PageRank 
References 
1
0.35
9
Authors
2
Name
Order
Citations
PageRank
Wojciech Czech1101.93
David A. Yuen28214.75