Research Article
Parallel Computing for Analysis of Large Scale Social Network
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 181 - Issue 24 |
| Published: Oct 2018 |
| Authors: Badrun Nahar Khan, Noor Mohammad Zahid |
10.5120/ijca2018917972
|
Badrun Nahar Khan, Noor Mohammad Zahid . Parallel Computing for Analysis of Large Scale Social Network. International Journal of Computer Applications. 181, 24 (Oct 2018), 17-18. DOI=10.5120/ijca2018917972
@article{ 10.5120/ijca2018917972,
author = { Badrun Nahar Khan,Noor Mohammad Zahid },
title = { Parallel Computing for Analysis of Large Scale Social Network },
journal = { International Journal of Computer Applications },
year = { 2018 },
volume = { 181 },
number = { 24 },
pages = { 17-18 },
doi = { 10.5120/ijca2018917972 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2018
%A Badrun Nahar Khan
%A Noor Mohammad Zahid
%T Parallel Computing for Analysis of Large Scale Social Network%T
%J International Journal of Computer Applications
%V 181
%N 24
%P 17-18
%R 10.5120/ijca2018917972
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The importance of social network analysis is realized as an inevitable tool in forthcoming years. This is due to the unprecedented growth of social-related data, boosted by the proliferation of social media websites and the embedded heterogeneity and complexity. The data generated from social network 10-15% data among them are structured and 85-90% data are unstructured. The unstructured data are useless. We will need additional technique to process those unstructured data. This paper focuses on parallel computational techniques for social network analysis. In particular, a brief discussion of some existing parallel algorithms is carried out and a new parallel computational technique is proposed to achieve parallelism.
References
- Chaffey, Dr. Dave (2018, March 28). How digitally mature is your business ? World Economic Forum
- Snijders, C., Martzat, U., & Reips, U.-D. “Big Data”: Big Gaps of Knowledge in the Field of Internet Science, International Journal of Internet Science, 2012
- Sanders, Peter and Schulz, Christian, “Think Locally, Act Globally: Highly Balanced Graph Partitioning”, International Symposium on Experimental Algorithms, 2013
- Andreev, Konstantin and Racke, Harald, “Balanced Graph Partitioning”
- George Cybenko, Parallel Computing for Machine Learning in Social Network Analysis
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