Crawling and Detecting Community Structure in Online Social Networks using Local Information

Size: px
Start display at page:

Download "Crawling and Detecting Community Structure in Online Social Networks using Local Information"

Transcription

1 Crawling and Detecting Community Structure in Online Social Networks using Local Information TU Delft - Network Architectures and Services (NAS) 1/12

2 Outline In order to find communities in a graph one needs the full graph. Crawling large Datasets like Online Social Networks takes very long. Facebook: 901 million (active April 2012), Twitter: Over 140 million (active March 2012) Ideal Crawling with one PC: 1s per request: Facebook 29years, Twitter: 4,5years 1. Crawling BFS/DFS/RFS Mutual Friend Crawling (MFC) the Reference Score Performance 2. Community Detection The Reference Score Compared to well known methods 3. Conclusion 2/12

3 Crawling Online Social Networks via Breadth/Depth first Search i i i 2 n standard Breadth First Search But unfortunately Social Networks are not tree like standard Depth First Search What most people do (Random First Search RFS) using a BFS/DFS/RFS leads to a sampling bias by using any of these methods and the fact one has to wait until the full graph is crawled to detect communities. 3/12

4 Crawling Online Social Networks via Mutual Friend Crawling Our proposed method Mutual Friend Crawling (MFC) overcomes this situation by crawling a Graph from any given seed point, Community wise. MFC is based on BFS/DFS plus one assumption: the degree of neighboring nodes is known and keeps a Reference Score S R This in the search trajectory the next node to be next node to visit is the one having the highest S R 4/12

5 Crawling Online Social Networks via Mutual Friend Crawling Example: Starting with node 2: its neighbors are 0,1,3,4 with degrees Lets take 4 the Reference Scores are: 0:0.2, 1:0.2, 3:0.25, 4:0.2 5/12

6 Crawling Online Social Networks via Mutual Friend Crawling - Performance BFS (blue) DFS(green) MFC(red) American Football network (Newman et al.) 6/12

7 Community Detection in OSNs via Mutual Friend Crawling How is the reference Score behaving while MFC is traversing the graph. As there MFC stays in communities the reference score is always increasing denoting that the community is tightly connected. As soon as there is a drop in S R a new community is been found. This drop is largest if an expressed community structure can be found. Otherwise it will be small 7/12

8 Community Detection in Online Social Networks via Mutual Friend Crawling Problem of misclassification If starting with a hub (11), the nodes 10 and 21 are classified as being in the same community as node 11 (the first community). Solution: after finishing a community check if the nodes in this community should really be in this community 8/12

9 Conclusion & Future Work We proposed an algorithm to crawl online social networks community wise in order to minimize sampling bias in communities. to be able to analyze data while still crawling the network The algorithm detects communities, (even for directed and weighted graphs) Future work: overlapping communities formalism to understand the drop in the reference score in order to catch how structured a graph is. (compared to modularity) 9/12

10 Thank you for your attention Questions Delft University of Technology Faculty of Electr. Engineering Dept. of Telecommunication Mekelweg CD Delft The Netherlands Room: EWI /12

11 Crawling Online Social Networks via Mutual Friend Crawling - Performance In order to measure the performance we were looking for ground truth datasets As it is very hard to find some real world datasets where the community partition is known we came up with a Cluster Graph Generator 1. node generation and slot assignment 2. assigning nodes to clusters 3. creating the links 4. force the generation of a giant connected component (GCC) Has the possibility to generate arbitrary (predefined) community size distributions Multiple community detection algorithms were tested on the ground truth 11/12

Efficient Crawling of Community Structures in Online Social Networks

Efficient Crawling of Community Structures in Online Social Networks Efficient Crawling of Community Structures in Online Social Networks Network Architectures and Services PVM 2011-071 Efficient Crawling of Community Structures in Online Social Networks For the degree

More information

Network Architectures & Services

Network Architectures & Services Network Architectures & Services Fernando Kuipers (F.A.Kuipers@tudelft.nl) Multi-dimensional analysis Network peopleware Network software Network hardware Individual: Quality of Experience Friends: Recommendation

More information

Entropy based Graph Clustering: Application to Biological and Social Networks

Entropy based Graph Clustering: Application to Biological and Social Networks Entropy based Graph Clustering: Application to Biological and Social Networks Edward C Kenley Young-Rae Cho Department of Computer Science Baylor University Complex Systems Definition Dynamically evolving

More information

Strong and Weak Ties

Strong and Weak Ties Strong and Weak Ties Web Science (VU) (707.000) Elisabeth Lex KTI, TU Graz April 11, 2016 Elisabeth Lex (KTI, TU Graz) Networks April 11, 2016 1 / 66 Outline 1 Repetition 2 Strong and Weak Ties 3 General

More information

Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network

Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network , pp.273-284 http://dx.doi.org/10.14257/ijdta.2015.8.5.24 Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network Gengxin Sun 1, Sheng Bin 2 and

More information

Understanding Graph Sampling Algorithms for Social Network Analysis

Understanding Graph Sampling Algorithms for Social Network Analysis Understanding Graph Sampling Algorithms for Social Network Analysis Tianyi Wang, Yang Chen 2, Zengbin Zhang 3, Tianyin Xu 2 Long Jin, Pan Hui 4, Beixing Deng, Xing Li Department of Electronic Engineering,

More information

Lecture 13: Validation

Lecture 13: Validation Lecture 3: Validation g Motivation g The Holdout g Re-sampling techniques g Three-way data splits Motivation g Validation techniques are motivated by two fundamental problems in pattern recognition: model

More information

Mining Social Network Graphs

Mining Social Network Graphs Mining Social Network Graphs Debapriyo Majumdar Data Mining Fall 2014 Indian Statistical Institute Kolkata November 13, 17, 2014 Social Network No introduc+on required Really? We s7ll need to understand

More information

An Alternative Web Search Strategy? Abstract

An Alternative Web Search Strategy? Abstract An Alternative Web Search Strategy? V.-H. Winterer, Rechenzentrum Universität Freiburg (Dated: November 2007) Abstract We propose an alternative Web search strategy taking advantage of the knowledge on

More information

Graph Analytics in Big Data. John Feo Pacific Northwest National Laboratory

Graph Analytics in Big Data. John Feo Pacific Northwest National Laboratory Graph Analytics in Big Data John Feo Pacific Northwest National Laboratory 1 A changing World The breadth of problems requiring graph analytics is growing rapidly Large Network Systems Social Networks

More information

SCAN: A Structural Clustering Algorithm for Networks

SCAN: A Structural Clustering Algorithm for Networks SCAN: A Structural Clustering Algorithm for Networks Xiaowei Xu, Nurcan Yuruk, Zhidan Feng (University of Arkansas at Little Rock) Thomas A. J. Schweiger (Acxiom Corporation) Networks scaling: #edges connected

More information

FPGA area allocation for parallel C applications

FPGA area allocation for parallel C applications 1 FPGA area allocation for parallel C applications Vlad-Mihai Sima, Elena Moscu Panainte, Koen Bertels Computer Engineering Faculty of Electrical Engineering, Mathematics and Computer Science Delft University

More information

SIP Service Providers and The Spam Problem

SIP Service Providers and The Spam Problem SIP Service Providers and The Spam Problem Y. Rebahi, D. Sisalem Fraunhofer Institut Fokus Kaiserin-Augusta-Allee 1 10589 Berlin, Germany {rebahi, sisalem}@fokus.fraunhofer.de Abstract The Session Initiation

More information

Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware

Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware Mahyar Shahsavari, Zaid Al-Ars, Koen Bertels,1, Computer Engineering Group, Software & Computer Technology

More information

Xiaoqiao Meng, Vasileios Pappas, Li Zhang IBM T.J. Watson Research Center Presented by: Payman Khani

Xiaoqiao Meng, Vasileios Pappas, Li Zhang IBM T.J. Watson Research Center Presented by: Payman Khani Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement Xiaoqiao Meng, Vasileios Pappas, Li Zhang IBM T.J. Watson Research Center Presented by: Payman Khani Overview:

More information

Data mining and statistical models in marketing campaigns of BT Retail

Data mining and statistical models in marketing campaigns of BT Retail Data mining and statistical models in marketing campaigns of BT Retail Francesco Vivarelli and Martyn Johnson Database Exploitation, Segmentation and Targeting group BT Retail Pp501 Holborn centre 120

More information

Mining Social-Network Graphs

Mining Social-Network Graphs 342 Chapter 10 Mining Social-Network Graphs There is much information to be gained by analyzing the large-scale data that is derived from social networks. The best-known example of a social network is

More information

Prediction of DDoS Attack Scheme

Prediction of DDoS Attack Scheme Chapter 5 Prediction of DDoS Attack Scheme Distributed denial of service attack can be launched by malicious nodes participating in the attack, exploit the lack of entry point in a wireless network, and

More information

Data Mining with R. Decision Trees and Random Forests. Hugh Murrell

Data Mining with R. Decision Trees and Random Forests. Hugh Murrell Data Mining with R Decision Trees and Random Forests Hugh Murrell reference books These slides are based on a book by Graham Williams: Data Mining with Rattle and R, The Art of Excavating Data for Knowledge

More information

Improving performance of Memory Based Reasoning model using Weight of Evidence coded categorical variables

Improving performance of Memory Based Reasoning model using Weight of Evidence coded categorical variables Paper 10961-2016 Improving performance of Memory Based Reasoning model using Weight of Evidence coded categorical variables Vinoth Kumar Raja, Vignesh Dhanabal and Dr. Goutam Chakraborty, Oklahoma State

More information

Proposed Advance Taxi Recommender System Based On a Spatiotemporal Factor Analysis Model

Proposed Advance Taxi Recommender System Based On a Spatiotemporal Factor Analysis Model Proposed Advance Taxi Recommender System Based On a Spatiotemporal Factor Analysis Model Santosh Thakkar, Supriya Bhosale, Namrata Gawade, Prof. Sonia Mehta Department of Computer Engineering, Alard College

More information

An Intelligent Matching System for the Products of Small Business/Manufactures with the Celebrities

An Intelligent Matching System for the Products of Small Business/Manufactures with the Celebrities An Intelligent Matching System for the Products of Small Business/Manufactures with the Celebrities Junho Jeong 1, Yunsik Son 2, Seokhoon Ko 1 and Seman Oh 1 1 Dept. of Computer Engineering, Dongguk University,

More information

Distributed Computing over Communication Networks: Maximal Independent Set

Distributed Computing over Communication Networks: Maximal Independent Set Distributed Computing over Communication Networks: Maximal Independent Set What is a MIS? MIS An independent set (IS) of an undirected graph is a subset U of nodes such that no two nodes in U are adjacent.

More information

Asking Hard Graph Questions. Paul Burkhardt. February 3, 2014

Asking Hard Graph Questions. Paul Burkhardt. February 3, 2014 Beyond Watson: Predictive Analytics and Big Data U.S. National Security Agency Research Directorate - R6 Technical Report February 3, 2014 300 years before Watson there was Euler! The first (Jeopardy!)

More information

SAFARI. Future Work Ideas. Alberto Garcia-Robledo, Abel Sanchez, Rongsha Li, Juan-Carlos Murillo-Torres, John Williams and Sascha Boheme

SAFARI. Future Work Ideas. Alberto Garcia-Robledo, Abel Sanchez, Rongsha Li, Juan-Carlos Murillo-Torres, John Williams and Sascha Boheme SAFARI Future Work Ideas Alberto Garcia-Robledo, Abel Sanchez, Rongsha Li, Juan-Carlos Murillo-Torres, John Williams and Sascha Boheme Massachusetts Institute of Technology z 1 Situational Awareness for

More information

1872 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 29, NO. 9, OCTOBER 2011

1872 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 29, NO. 9, OCTOBER 2011 872 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 29, NO. 9, OCTOBER 20 Practical Recommendations on Crawling Online Social Networks Minas Gjoka, Maciej Kurant, Carter T. Butts, and Athina Markopoulou,

More information

Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science IBM Chief Scientist, Graph Computing. October 29th, 2015

Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science IBM Chief Scientist, Graph Computing. October 29th, 2015 E6893 Big Data Analytics Lecture 8: Spark Streams and Graph Computing (I) Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science IBM Chief Scientist, Graph Computing

More information

Client Overview. Engagement Situation. Key Requirements

Client Overview. Engagement Situation. Key Requirements Client Overview Our client is one of the leading providers of business intelligence systems for customers especially in BFSI space that needs intensive data analysis of huge amounts of data for their decision

More information

Visualization methods for patent data

Visualization methods for patent data Visualization methods for patent data Treparel 2013 Dr. Anton Heijs (CTO & Founder) Delft, The Netherlands Introduction Treparel can provide advanced visualizations for patent data. This document describes

More information

LOAD BALANCING AND EFFICIENT CLUSTERING FOR IMPROVING NETWORK PERFORMANCE IN AD-HOC NETWORKS

LOAD BALANCING AND EFFICIENT CLUSTERING FOR IMPROVING NETWORK PERFORMANCE IN AD-HOC NETWORKS LOAD BALANCING AND EFFICIENT CLUSTERING FOR IMPROVING NETWORK PERFORMANCE IN AD-HOC NETWORKS Saranya.S 1, Menakambal.S 2 1 M.E., Embedded System Technologies, Nandha Engineering College (Autonomous), (India)

More information

Expanding the CASEsim Framework to Facilitate Load Balancing of Social Network Simulations

Expanding the CASEsim Framework to Facilitate Load Balancing of Social Network Simulations Expanding the CASEsim Framework to Facilitate Load Balancing of Social Network Simulations Amara Keller, Martin Kelly, Aaron Todd 4 June 2010 Abstract This research has two components, both involving the

More information

Social Media Mining. Data Mining Essentials

Social Media Mining. Data Mining Essentials Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

More information

Load Balancing. Load Balancing 1 / 24

Load Balancing. Load Balancing 1 / 24 Load Balancing Backtracking, branch & bound and alpha-beta pruning: how to assign work to idle processes without much communication? Additionally for alpha-beta pruning: implementing the young-brothers-wait

More information

Sampling Online Social Networks

Sampling Online Social Networks Sampling Online Social Networks Athina Markopoulou 1,3 Joint work with: Minas Gjoka 3, Maciej Kurant 3, Carter T. Butts 2,3, Patrick Thiran 4 1 Department of Electrical Engineering and Computer Science

More information

Attacking Anonymized Social Network

Attacking Anonymized Social Network Attacking Anonymized Social Network From: Wherefore Art Thou RX3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography Presented By: Machigar Ongtang (Ongtang@cse.psu.edu ) Social

More information

Determining optimum insurance product portfolio through predictive analytics BADM Final Project Report

Determining optimum insurance product portfolio through predictive analytics BADM Final Project Report 2012 Determining optimum insurance product portfolio through predictive analytics BADM Final Project Report Dinesh Ganti(61310071), Gauri Singh(61310560), Ravi Shankar(61310210), Shouri Kamtala(61310215),

More information

Graphalytics: A Big Data Benchmark for Graph-Processing Platforms

Graphalytics: A Big Data Benchmark for Graph-Processing Platforms Graphalytics: A Big Data Benchmark for Graph-Processing Platforms Mihai Capotă, Tim Hegeman, Alexandru Iosup, Arnau Prat-Pérez, Orri Erling, Peter Boncz Delft University of Technology Universitat Politècnica

More information

So, how do you pronounce. Jilles Vreeken. Okay, now we can talk. So, what kind of data? binary. * multi-relational

So, how do you pronounce. Jilles Vreeken. Okay, now we can talk. So, what kind of data? binary. * multi-relational Simply Mining Data Jilles Vreeken So, how do you pronounce Exploratory Data Analysis Jilles Vreeken Jilles Yill less Vreeken Fray can 17 August 2015 Okay, now we can talk. 17 August 2015 The goal So, what

More information

CAB TRAVEL TIME PREDICTI - BASED ON HISTORICAL TRIP OBSERVATION

CAB TRAVEL TIME PREDICTI - BASED ON HISTORICAL TRIP OBSERVATION CAB TRAVEL TIME PREDICTI - BASED ON HISTORICAL TRIP OBSERVATION N PROBLEM DEFINITION Opportunity New Booking - Time of Arrival Shortest Route (Distance/Time) Taxi-Passenger Demand Distribution Value Accurate

More information

Implementing Graph Pattern Mining for Big Data in the Cloud

Implementing Graph Pattern Mining for Big Data in the Cloud Implementing Graph Pattern Mining for Big Data in the Cloud Chandana Ojah M.Tech in Computer Science & Engineering Department of Computer Science & Engineering, PES College of Engineering, Mandya Ojah.chandana@gmail.com

More information

W6.B.1. FAQs CS535 BIG DATA W6.B.3. 4. If the distance of the point is additionally less than the tight distance T 2, remove it from the original set

W6.B.1. FAQs CS535 BIG DATA W6.B.3. 4. If the distance of the point is additionally less than the tight distance T 2, remove it from the original set http://wwwcscolostateedu/~cs535 W6B W6B2 CS535 BIG DAA FAQs Please prepare for the last minute rush Store your output files safely Partial score will be given for the output from less than 50GB input Computer

More information

Scheduling in a Virtual Enterprise in the Service Sector

Scheduling in a Virtual Enterprise in the Service Sector Scheduling in a Virtual Enterprise in the Service Sector Florian Kandler Electronic Commerce Competence Center, Donau-City-Strasse, 7 Vienna, Austria florian.kandler@ec,at http://www.ec.at/ Abstract. The

More information

IBA Business Analytics Data Challenge

IBA Business Analytics Data Challenge Information is the oil of the 21st century, and analytics is the combustion engine." - Peter Sondergaard, SVP, Gartner Research October 31 st, 2014 IBA Business Analytics Data Challenge Atur, Ramanuja

More information

Graph Theory and Complex Networks: An Introduction. Chapter 08: Computer networks

Graph Theory and Complex Networks: An Introduction. Chapter 08: Computer networks Graph Theory and Complex Networks: An Introduction Maarten van Steen VU Amsterdam, Dept. Computer Science Room R4.20, steen@cs.vu.nl Chapter 08: Computer networks Version: March 3, 2011 2 / 53 Contents

More information

Chapter 12 Bagging and Random Forests

Chapter 12 Bagging and Random Forests Chapter 12 Bagging and Random Forests Xiaogang Su Department of Statistics and Actuarial Science University of Central Florida - 1 - Outline A brief introduction to the bootstrap Bagging: basic concepts

More information

Cloud Computing. Lectures 10 and 11 Map Reduce: System Perspective 2014-2015

Cloud Computing. Lectures 10 and 11 Map Reduce: System Perspective 2014-2015 Cloud Computing Lectures 10 and 11 Map Reduce: System Perspective 2014-2015 1 MapReduce in More Detail 2 Master (i) Execution is controlled by the master process: Input data are split into 64MB blocks.

More information

Sentiment analysis using emoticons

Sentiment analysis using emoticons Sentiment analysis using emoticons Royden Kayhan Lewis Moharreri Steven Royden Ware Lewis Kayhan Steven Moharreri Ware Department of Computer Science, Ohio State University Problem definition Our aim was

More information

A1 and FARM scalable graph database on top of a transactional memory layer

A1 and FARM scalable graph database on top of a transactional memory layer A1 and FARM scalable graph database on top of a transactional memory layer Miguel Castro, Aleksandar Dragojević, Dushyanth Narayanan, Ed Nightingale, Alex Shamis Richie Khanna, Matt Renzelmann Chiranjeeb

More information

MALLET-Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph

MALLET-Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph MALLET-Privacy Preserving Influencer Mining in Social Media Networks via Hypergraph Janani K 1, Narmatha S 2 Assistant Professor, Department of Computer Science and Engineering, Sri Shakthi Institute of

More information

TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM

TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM Thanh-Nghi Do College of Information Technology, Cantho University 1 Ly Tu Trong Street, Ninh Kieu District Cantho City, Vietnam

More information

Creating a Network Graph with Gephi

Creating a Network Graph with Gephi Creating a Network Graph with Gephi Gephi is a powerful tool for network analysis, but it can be intimidating. It has a lot of tools for statistical analysis of network data most of which you won't be

More information

Load Balancing Techniques

Load Balancing Techniques Load Balancing Techniques 1 Lecture Outline Following Topics will be discussed Static Load Balancing Dynamic Load Balancing Mapping for load balancing Minimizing Interaction 2 1 Load Balancing Techniques

More information

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK A REVIEW ON THE USAGE OF OLD AND NEW DATA STRUCTURE ARRAYS, LINKED LIST, STACK,

More information

Application of Social Network Analysis to Collaborative Team Formation

Application of Social Network Analysis to Collaborative Team Formation Application of Social Network Analysis to Collaborative Team Formation Michelle Cheatham Kevin Cleereman Information Directorate Information Directorate AFRL AFRL WPAFB, OH 45433 WPAFB, OH 45433 michelle.cheatham@wpafb.af.mil

More information

10-810 /02-710 Computational Genomics. Clustering expression data

10-810 /02-710 Computational Genomics. Clustering expression data 10-810 /02-710 Computational Genomics Clustering expression data What is Clustering? Organizing data into clusters such that there is high intra-cluster similarity low inter-cluster similarity Informally,

More information

Smart Sell Re-quote project for an Insurance company.

Smart Sell Re-quote project for an Insurance company. SAS Analytics Day Smart Sell Re-quote project for an Insurance company. A project by Ajay Guyyala Naga Sudhir Lanka Narendra Babu Merla Kiran Reddy Samiullah Bramhanapalli Shaik Business Situation XYZ

More information

Clustering online social network communities using genetic algorithms

Clustering online social network communities using genetic algorithms Clustering online social network communities using genetic algorithms Mustafa H. Hajeer * Alka Singh * Dipankar Dasgupta * Sugata Sanyal # * Center for Information Assurance Department of Computer Science

More information

DATA ANALYSIS II. Matrix Algorithms

DATA ANALYSIS II. Matrix Algorithms DATA ANALYSIS II Matrix Algorithms Similarity Matrix Given a dataset D = {x i }, i=1,..,n consisting of n points in R d, let A denote the n n symmetric similarity matrix between the points, given as where

More information

Chapter 29 Scale-Free Network Topologies with Clustering Similar to Online Social Networks

Chapter 29 Scale-Free Network Topologies with Clustering Similar to Online Social Networks Chapter 29 Scale-Free Network Topologies with Clustering Similar to Online Social Networks Imre Varga Abstract In this paper I propose a novel method to model real online social networks where the growing

More information

A Locality Enhanced Scheduling Method for Multiple MapReduce Jobs In a Workflow Application

A Locality Enhanced Scheduling Method for Multiple MapReduce Jobs In a Workflow Application 2012 International Conference on Information and Computer Applications (ICICA 2012) IPCSIT vol. 24 (2012) (2012) IACSIT Press, Singapore A Locality Enhanced Scheduling Method for Multiple MapReduce Jobs

More information

ALBERTA. Social Network Analysis for the Assessment of Learning UNIVERSITY OF. Osmar R. Zaïane Professor & Scientific Director of AICML

ALBERTA. Social Network Analysis for the Assessment of Learning UNIVERSITY OF. Osmar R. Zaïane Professor & Scientific Director of AICML UNIVERSITY OF ALBERTA Social Network Analysis for the Assessment of Learning Osmar R. Zaïane Professor & Scientific Director of AICML Educational Data Mining 2010 Pittsburgh, USA University of Alberta

More information

Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus

Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus 1. Introduction Facebook is a social networking website with an open platform that enables developers to extract and utilize user information

More information

Uncovering nodes that spread information between communities in social networks

Uncovering nodes that spread information between communities in social networks Mantzaris EPJ Data Science 2014, 3:26 REGULAR ARTICLE OpenAccess Uncovering nodes that spread information between communities in social networks Alexander V Mantzaris * * Correspondence: alexander.mantzaris@strath.ac.uk

More information

! E6893 Big Data Analytics Lecture 9:! Linked Big Data Graph Computing (I)

! E6893 Big Data Analytics Lecture 9:! Linked Big Data Graph Computing (I) ! E6893 Big Data Analytics Lecture 9:! Linked Big Data Graph Computing (I) Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science Mgr., Dept. of Network Science and

More information

Binary Search Trees CMPSC 122

Binary Search Trees CMPSC 122 Binary Search Trees CMPSC 122 Note: This notes packet has significant overlap with the first set of trees notes I do in CMPSC 360, but goes into much greater depth on turning BSTs into pseudocode than

More information

Link Prediction in Social Networks

Link Prediction in Social Networks CS378 Data Mining Final Project Report Dustin Ho : dsh544 Eric Shrewsberry : eas2389 Link Prediction in Social Networks 1. Introduction Social networks are becoming increasingly more prevalent in the daily

More information

Role of Neural network in data mining

Role of Neural network in data mining Role of Neural network in data mining Chitranjanjit kaur Associate Prof Guru Nanak College, Sukhchainana Phagwara,(GNDU) Punjab, India Pooja kapoor Associate Prof Swami Sarvanand Group Of Institutes Dinanagar(PTU)

More information

In the following we will only consider undirected networks.

In the following we will only consider undirected networks. Roles in Networks Roles in Networks Motivation for work: Let topology define network roles. Work by Kleinberg on directed graphs, used topology to define two types of roles: authorities and hubs. (Each

More information

PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce. Authors: B. Panda, J. S. Herbach, S. Basu, R. J. Bayardo.

PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce. Authors: B. Panda, J. S. Herbach, S. Basu, R. J. Bayardo. PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce Authors: B. Panda, J. S. Herbach, S. Basu, R. J. Bayardo. VLDB 2009 CS 422 Decision Trees: Main Components Find Best Split Choose split

More information

Data Mining Algorithms Part 1. Dejan Sarka

Data Mining Algorithms Part 1. Dejan Sarka Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses

More information

Load-Balanced Migration of Social Media to Content Clouds

Load-Balanced Migration of Social Media to Content Clouds Load-Balanced Migration of Social Media to Content Clouds ABSTRACT Xu Cheng School of Computing Science Simon Fraser University British Columbia, Canada xuc@cs.sfu.ca Social networked applications have

More information

Krishna Institute of Engineering & Technology, Ghaziabad Department of Computer Application MCA-213 : DATA STRUCTURES USING C

Krishna Institute of Engineering & Technology, Ghaziabad Department of Computer Application MCA-213 : DATA STRUCTURES USING C Tutorial#1 Q 1:- Explain the terms data, elementary item, entity, primary key, domain, attribute and information? Also give examples in support of your answer? Q 2:- What is a Data Type? Differentiate

More information

MBA - INFORMATION TECHNOLOGY MANAGEMENT (MBAITM) Term-End Examination December, 2014 MBMI-012 : BUSINESS INTELLIGENCE SECTION I

MBA - INFORMATION TECHNOLOGY MANAGEMENT (MBAITM) Term-End Examination December, 2014 MBMI-012 : BUSINESS INTELLIGENCE SECTION I No. of Printed Pages : 8 I MBMI-012 I MBA - INFORMATION TECHNOLOGY MANAGEMENT (MBAITM) Term-End Examination December, 2014 Time : 3 hours Note : (i) (ii) (iii) (iv) (v) MBMI-012 : BUSINESS INTELLIGENCE

More information

Efficient Parallel Graph Exploration on Multi-Core CPU and GPU

Efficient Parallel Graph Exploration on Multi-Core CPU and GPU Efficient Parallel Graph Exploration on Multi-Core CPU and GPU Pervasive Parallelism Laboratory Stanford University Sungpack Hong, Tayo Oguntebi, and Kunle Olukotun Graph and its Applications Graph Fundamental

More information

Bachelor of Bachelor of Computer Science

Bachelor of Bachelor of Computer Science Bachelor of Bachelor of Computer Science Detailed Course Requirements The 2016 Monash University Handbook will be available from October 2015. This document contains interim 2016 course requirements information.

More information

Parallelization: Binary Tree Traversal

Parallelization: Binary Tree Traversal By Aaron Weeden and Patrick Royal Shodor Education Foundation, Inc. August 2012 Introduction: According to Moore s law, the number of transistors on a computer chip doubles roughly every two years. First

More information

CS 6220: Data Mining Techniques Course Project Description

CS 6220: Data Mining Techniques Course Project Description CS 6220: Data Mining Techniques Course Project Description College of Computer and Information Science Northeastern University Spring 2013 General Goal In this project, you will have an opportunity to

More information

Minimize Response Time Using Distance Based Load Balancer Selection Scheme

Minimize Response Time Using Distance Based Load Balancer Selection Scheme Minimize Response Time Using Distance Based Load Balancer Selection Scheme K. Durga Priyanka M.Tech CSE Dept., Institute of Aeronautical Engineering, HYD-500043, Andhra Pradesh, India. Dr.N. Chandra Sekhar

More information

Data Mining with SQL Server Data Tools

Data Mining with SQL Server Data Tools Data Mining with SQL Server Data Tools Data mining tasks include classification (directed/supervised) models as well as (undirected/unsupervised) models of association analysis and clustering. 1 Data Mining

More information

Single machine models: Maximum Lateness -12- Approximation ratio for EDD for problem 1 r j,d j < 0 L max. structure of a schedule Q...

Single machine models: Maximum Lateness -12- Approximation ratio for EDD for problem 1 r j,d j < 0 L max. structure of a schedule Q... Lecture 4 Scheduling 1 Single machine models: Maximum Lateness -12- Approximation ratio for EDD for problem 1 r j,d j < 0 L max structure of a schedule 0 Q 1100 11 00 11 000 111 0 0 1 1 00 11 00 11 00

More information

SOCIAL MEDIA 80 78 76 74 72 70 68 66 64 Access to free content Series 1 To learn Advanced news of products Series 1 A Social Roadmap Understand how and why people use social media Map the social

More information

Applying Data Analysis to Big Data Benchmarks. Jazmine Olinger

Applying Data Analysis to Big Data Benchmarks. Jazmine Olinger Applying Data Analysis to Big Data Benchmarks Jazmine Olinger Abstract This paper describes finding accurate and fast ways to simulate Big Data benchmarks. Specifically, using the currently existing simulation

More information

Social Media Mining. Graph Essentials

Social Media Mining. Graph Essentials Graph Essentials Graph Basics Measures Graph and Essentials Metrics 2 2 Nodes and Edges A network is a graph nodes, actors, or vertices (plural of vertex) Connections, edges or ties Edge Node Measures

More information

Protein Protein Interaction Networks

Protein Protein Interaction Networks Functional Pattern Mining from Genome Scale Protein Protein Interaction Networks Young-Rae Cho, Ph.D. Assistant Professor Department of Computer Science Baylor University it My Definition of Bioinformatics

More information

Impelling Heart Attack Prediction System using Data Mining and Artificial Neural Network

Impelling Heart Attack Prediction System using Data Mining and Artificial Neural Network General Article International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347-5161 2014 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Impelling

More information

Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2. Tid Refund Marital Status

Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2. Tid Refund Marital Status Data Mining Classification: Basic Concepts, Decision Trees, and Evaluation Lecture tes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar Classification: Definition Given a collection of

More information

project collects data from national events, both natural and manmade, to be stored and evaluated by

project collects data from national events, both natural and manmade, to be stored and evaluated by Joseph Sebastian CS 2994 Spring 2014 Undergraduate Research Final Paper GOALS The goal of my research was to assist the Integrated Digital Event Archive (IDEAL) team in transferring their Twitter data

More information

6.2.8 Neural networks for data mining

6.2.8 Neural networks for data mining 6.2.8 Neural networks for data mining Walter Kosters 1 In many application areas neural networks are known to be valuable tools. This also holds for data mining. In this chapter we discuss the use of neural

More information

Predictive Dynamix Inc

Predictive Dynamix Inc Predictive Modeling Technology Predictive modeling is concerned with analyzing patterns and trends in historical and operational data in order to transform data into actionable decisions. This is accomplished

More information

Cross-validation for detecting and preventing overfitting

Cross-validation for detecting and preventing overfitting Cross-validation for detecting and preventing overfitting Note to other teachers and users of these slides. Andrew would be delighted if ou found this source material useful in giving our own lectures.

More information

Understanding Neo4j Scalability

Understanding Neo4j Scalability Understanding Neo4j Scalability David Montag January 2013 Understanding Neo4j Scalability Scalability means different things to different people. Common traits associated include: 1. Redundancy in the

More information

Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

More information

An Analysis of Social Network-Based Sybil Defenses

An Analysis of Social Network-Based Sybil Defenses An Analysis of Social Network-Based Sybil Defenses ABSTRACT Bimal Viswanath MPI-SWS bviswana@mpi-sws.org Krishna P. Gummadi MPI-SWS gummadi@mpi-sws.org Recently, there has been much excitement in the research

More information

Voice of the Customers: Mining Online Customer Reviews for Product Feature-Based Ranking

Voice of the Customers: Mining Online Customer Reviews for Product Feature-Based Ranking Voice of the Customers: Mining Online Customer Reviews for Product Feature-Based Ranking Kunpeng Zhang, Ramanathan Narayanan, Alok Choudhary Dept. of Electrical Engineering and Computer Science Center

More information

IMPROVED FAIR SCHEDULING ALGORITHM FOR TASKTRACKER IN HADOOP MAP-REDUCE

IMPROVED FAIR SCHEDULING ALGORITHM FOR TASKTRACKER IN HADOOP MAP-REDUCE IMPROVED FAIR SCHEDULING ALGORITHM FOR TASKTRACKER IN HADOOP MAP-REDUCE Mr. Santhosh S 1, Mr. Hemanth Kumar G 2 1 PG Scholor, 2 Asst. Professor, Dept. Of Computer Science & Engg, NMAMIT, (India) ABSTRACT

More information

Data Mining Fundamentals

Data Mining Fundamentals Part I Data Mining Fundamentals Data Mining: A First View Chapter 1 1.11 Data Mining: A Definition Data Mining The process of employing one or more computer learning techniques to automatically analyze

More information

Data Mining Classification: Decision Trees

Data Mining Classification: Decision Trees Data Mining Classification: Decision Trees Classification Decision Trees: what they are and how they work Hunt s (TDIDT) algorithm How to select the best split How to handle Inconsistent data Continuous

More information

Clustering UE 141 Spring 2013

Clustering UE 141 Spring 2013 Clustering UE 141 Spring 013 Jing Gao SUNY Buffalo 1 Definition of Clustering Finding groups of obects such that the obects in a group will be similar (or related) to one another and different from (or

More information

Architectural Framework for Large- Scale Multicast in Mobile Ad Hoc Networks

Architectural Framework for Large- Scale Multicast in Mobile Ad Hoc Networks Architectural Framework for Large- Scale Multicast in Mobile Ad Hoc Networks Ahmed Helmy Electrical Engineering Department University of Southern California (USC) helmy@usc.edu http://ceng.usc.edu/~helmy

More information