SC12 Home > SC12 Schedule > SC12 Presentation - Analyzing Patterns in Large-Scale Graphs Using MapReduce in Hadoop

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

Add to iCal  Click here to download .ics calendar file

Add to Outlook  Click here to download .vcs calendar file

Add to Google Calendarss  Click here to add event to your Google Calendar