Title :
Performance of graph reconstruction method for large-scale web graph analysis
Author :
Ryota Takei;Ayahiko Niimi
Author_Institution :
Future University Hakodate, Hokkaido, Japan
Abstract :
We have already proposed a graph analysis method that could shorten the analysis time by reconstructing a web graph. In our proposed method, a web graph is reconstructed for parallel distributed processing of possible graphs by clustering a web graph and reconstructing the web graph for Compression Graph and Cluster Graphs. Compression Graph represents the relationship between clusters, whereas Cluster Graph contains nodes belonging to each cluster. When analyzing Compression Graph and Cluster Graphs, they can be processed in parallel because there is no relationship between Compression Graph and Cluster Graphs. Further examining our previous study in which we considered a small web graph, in the present paper, we discuss the performance of the proposed method on a large-scale web graph by experiments.
Keywords :
"Distributed processing","Clustering algorithms","Algorithm design and analysis","Data mining","Big data","Reconstruction algorithms","Electronic mail"
Conference_Titel :
Big Data (Big Data), 2015 IEEE International Conference on
DOI :
10.1109/BigData.2015.7364100