@inproceedings{03138930de5640d7afe48ae54a43e222,
title = "A semi-clustering scheme for large-scale graph analysis on hadoop",
abstract = "With the evolution of IT technologies, large-scale graph data have lately become a growing interest. As a result, there are a lot of research results in large-scale graph analysis on Hadoop. The graph analysis based on Hadoop provides parallel programming models with data partitioning and contains iterative phases of MapReduce jobs. Therefore, the effectiveness of data partitioning depends on how the data partitioning maintains data locality in each node of cluster. In this paper, we propose a semi-clustering scheme for large-scale graph analysis such as PageRank algorithm on Hadoop and show that the proposed scheme is effective. With experiment results, PageRank computation with the semi-clustering improves the performance.",
keywords = "Hadoop, large-scale graph analysis, PageRank, semi-clustering",
author = "Seungtae Hong and Youngsung Shin and Choi, \{Dong Hoon\} and Heeseung Jo and Chang, \{Jae Woo\}",
year = "2014",
doi = "10.1007/978-3-642-40675-1\_46",
language = "English",
isbn = "9783642406744",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "301--306",
booktitle = "Mobile, Ubiquitous, and Intelligent Computing, MUSIC 2013",
note = "4th International Conference on Mobile, Ubiquitous, and Intelligent Computing, MUSIC 2013 ; Conference date: 04-09-2013 Through 06-09-2013",
}