DocumentCode :
1918971
Title :
Abstract: Analyzing Patterns in Large-Scale Graphs Using MapReduce in Hadoop
Author :
Schultz, Jamie ; Vierya, J. ; Lu, Erl-Huei
Author_Institution :
Dept. of Math. & Comput. Sci., Salisbury Univ., Salisbury, MD, USA
fYear :
2012
fDate :
10-16 Nov. 2012
Firstpage :
1457
Lastpage :
1458
Abstract :
Analyzing patterns in large-scale graphs, such as social networks (e.g. Facebook, Linkedin, Twitter) has many applications including community identification, blog analysis, intrusion and spamming detections. Currently, it is impossible to process information in large-scale graphs with millions even billions of edges with a single computer. In this paper, we take advantage of MapReduce, a programming model for processing large datasets, to detect important graph patterns using open source Hadoop on Amazon EC2. The aim of this paper is to show how MapReduce cloud computing with the application of graph pattern detection scales on real world data. We implement Cohen´s MapReduce graph algorithms to enumerate patterns including triangles, rectangles, trusses and barycentric clusters using real world data taken from Snap Stanford. In addition, we create a visualization algorithm to visualize the detected graph patterns. The performance of MapReduce graph algorithms has been discussed too.
Keywords :
cloud computing; data visualisation; graphs; pattern recognition; security of data; social networking (online); Amazon EC2; Cohen MapReduce graph algorithms; Facebook; Linkedin; MapReduce cloud computing; Snap Stanford; Twitter; barycentric clusters; blog analysis; community identification; detected graph pattern visualizations; graph pattern detection scales; information processing; intrusion detections; large dataset processing; large-scale graphs; open source Hadoop; pattern analysis; programming model; social networks; spamming detections; visualization algorithm; Cloud Computing; Cohesive Components; Graph Algorithms; MapReduce; Pattern Detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:
Conference_Location :
Salt Lake City, UT
Print_ISBN :
978-1-4673-6218-4
Type :
conf
DOI :
10.1109/SC.Companion.2012.257
Filename :
6496040
Link To Document :
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