SCHEDULE: NOV 10-16, 2012
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Analyzing Patterns in Large-Scale Graphs Using MapReduce in Hadoop
SESSION: Research Poster Reception
EVENT TYPE: Posters and Electronic Posters
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Joshua Schultz, Jonathan Vierya, Enyue Lu
ROOM:East Entrance
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 Cohens 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.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Joshua Schultz - Salisbury University
Jonathan Vierya - California State Polytechnic University, Pomona
Enyue Lu - Salisbury University
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Analyzing Patterns in Large-Scale Graphs Using MapReduce in Hadoop
SESSION: Research Poster Reception
EVENT TYPE:
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Joshua Schultz, Jonathan Vierya, Enyue Lu
ROOM:East Entrance
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 Cohens 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.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Joshua Schultz - Salisbury University
Jonathan Vierya - California State Polytechnic University, Pomona
Enyue Lu - Salisbury University
Click here to download .ics calendar file