Overview of the Stateof-the-Art. Networks. Evolution of social network studies
|
|
- George Richards
- 6 years ago
- Views:
Transcription
1 Overview of the Stateof-the-Art in Social Networks INF5370 spring 2014 Evolution of social network studies : mathematical studies of networks formed by the actual human interactions Pandemics, speed of gossiping, advertisements 1977: 34 people in a Karate club First scientific study of an actual network [Zachary, J. Anth. Res. 1977] Two years of field work 2003: 436 people using a corporate system [Adamic and Adar, Social Networks 2003] 2006: 43,553 people using a university system [Kossinets and Watts, Science 2006] 2007: 4,400,000 people using an online blogging service [Backstrom et al., KDD 2007] 2009: 240,000,000 people using an instant messaging service [Leskovec and Horvitz, WWW 2009] 1
2 What are social networks about? We all know what it is about Chatting with friends Blogging Following people Discussing information of interest Sharing data among friends However A dozen of major reasons for people to use social networks Thousands of possible meaningful data filters Different acceptable ethical guidelines and cultural differences Different governmental policies about data exchange Hundreds of different implementations with varying functionality Are we going to converge? Analysis of relations and social graphs The meaning of a relation or a link Flickr: bookmark LinkedIn: send messages Facebook: view (some) content or mutual agreement Links can be established between Best friends Casual acquaintances People who like to publicly disagree with each other People who have not heard of each other Are social links valid indicators of real user interaction? <1% of people on Facebook talk to >50% of their friends 50% of users interact with less than 20% of their friends 2
3 Social networks are navigable An Experimental Study of the Small World Problem by Jeffery Travers and Stanley Milgram [Sociometry, vol.32 no ] Performed by tracking snail mail communication 6 degrees of separation: short chains do exist Not only short chains exist but people can find them Using only local knowledge and information about the target What made networks navigable and how do the users pick next hop? An Experimental Study of Search in Global Social Networks by Peter Sheridan Dodds, Roby Muhamad, and Duncan J. Watts [Science, vol. 301 no August 2003] Geography dominated early in the chain Work and education dominated later in the chain In successful chains, non-close ties chosen more Other characteristics of interest Links exhibit a high degree of symmetry Unlike web links Consequently in-degree is close to out-degree Results in a smaller network diameter The distribution of degrees follows the power law There exist a core in most studied social networks A minimal connected component whose removal disconnects the graph Was discovered to include 1-10% of all the nodes in a social network Most short paths go through the core Can be used for quickly disseminating information Low-degree users show high degree of clustering Users with few friends tend to form mini-cliques Clusters are connected by bridges 3
4 Taxonomy of existing social network applications Online social networks (OSN) Twitter, Facebook, livejournal, digg, del.ici.ous Collaborative editing (Wikipedia) Integrated discussions (GoogleWave) Mobile and ad-hoc social networks Virtual Collaborative Networks Delay-Tolerant Networks (DTNs) Collaborative streaming applications Integrated with multimedia content delivery (Tribler, Spotify) General and integrated solutions Anatomy of a social network application Frontend (functionality) Data communication middleware Backend (data storage) 4
5 Challenges of Implementing Social Networks Rich functionality Non-functional property Scalability Degree of privacy Security (resilience to attacks) Data availability Real-time guarantee (e.g., for tweets, RSS, streaming applications) Rationale for decentralizing social network applications Decentralization is inherent for mobile social networks Federation of a large number of existing social networks Interoperable social networks, each network following its own rules and regulations Privacy-oriented solutions No concentration of financially valuable data at any single location The end users are endowed with better control over their own data 5
6 Yet, decentralization poses additional challenges Distributed data mining is more challenging Large-scale decentralization diminishes the computational potential of large data centers Requires creation of a new infrastructure Even supporting search is not trivial Requires a different business model The focus of this course Data analysis Challenges and proposed technological solutions Particular focus on decentralized solutions 6
Functionality, Challenges and Architecture of Social Networks
Functionality, Challenges and Architecture of Social Networks INF 5370 Outline Social Network Services Functionality Business Model Current Architecture and Scalability Challenges Conclusion 1 Social Network
More informationTELCOM2125: Network Science and Analysis
School of Information Sciences University of Pittsburgh TELCOM2125: Network Science and Analysis Konstantinos Pelechrinis Spring 2015 Figures are taken from: M.E.J. Newman, Networks: An Introduction 2
More informationStructure of Social Networks
Structure of Social Networks Outline Structure of social networks Applications of structural analysis Social *networks* Twitter Facebook Linked-in IMs Email Real life Address books... Who Twitter #numbers
More informationChapter 1. Social Media and Social Computing. October 2012 Youn-Hee Han
Chapter 1. Social Media and Social Computing October 2012 Youn-Hee Han http://link.koreatech.ac.kr 1.1 Social Media A rapid development and change of the Web and the Internet Participatory web application
More informationExtracting Information from Complex Networks
Extracting Information from Complex Networks 1 Complex Networks Networks that arise from modeling complex systems: relationships Social networks Biological networks Distinguish from random networks uniform
More informationANNUAL REPORT Visit us at project.eu Supported by. Mission
Mission ANNUAL REPORT 2011 The Web has proved to be an unprecedented success for facilitating the publication, use and exchange of information, at planetary scale, on virtually every topic, and representing
More informationCSE 316: SOCIAL NETWORK ANALYSIS INTRODUCTION. Fall 2017 Marion Neumann
CSE 316: SOCIAL NETWORK ANALYSIS Fall 2017 Marion Neumann INTRODUCTION Contents in these slides may be subject to copyright. Some materials are adopted from: http://www.cs.cornell.edu/home /kleinber/ networks-book,
More informationCSE 190 Lecture 16. Data Mining and Predictive Analytics. Small-world phenomena
CSE 190 Lecture 16 Data Mining and Predictive Analytics Small-world phenomena Another famous study Stanley Milgram wanted to test the (already popular) hypothesis that people in social networks are separated
More informationAn Empirical Analysis of Communities in Real-World Networks
An Empirical Analysis of Communities in Real-World Networks Chuan Sheng Foo Computer Science Department Stanford University csfoo@cs.stanford.edu ABSTRACT Little work has been done on the characterization
More informationHow Do Real Networks Look? Networked Life NETS 112 Fall 2014 Prof. Michael Kearns
How Do Real Networks Look? Networked Life NETS 112 Fall 2014 Prof. Michael Kearns Roadmap Next several lectures: universal structural properties of networks Each large-scale network is unique microscopically,
More informationModule 1: Internet Basics for Web Development (II)
INTERNET & WEB APPLICATION DEVELOPMENT SWE 444 Fall Semester 2008-2009 (081) Module 1: Internet Basics for Web Development (II) Dr. El-Sayed El-Alfy Computer Science Department King Fahd University of
More informationCSE 158 Lecture 11. Web Mining and Recommender Systems. Triadic closure; strong & weak ties
CSE 158 Lecture 11 Web Mining and Recommender Systems Triadic closure; strong & weak ties Triangles So far we ve seen (a little about) how networks can be characterized by their connectivity patterns What
More informationSI Networks: Theory and Application, Fall 2008
University of Michigan Deep Blue deepblue.lib.umich.edu 2008-09 SI 508 - Networks: Theory and Application, Fall 2008 Adamic, Lada Adamic, L. (2008, November 12). Networks: Theory and Application. Retrieved
More informationTITLE SOCIAL MEDIA AND COLLABORATION POLICY
DATE 9/20/2010 TITLE 408.01 SOCIAL MEDIA AND COLLABORATION POLICY ORG. AGENCY Department of Communications Approved AFT As more and more citizens in our community make the shift towards, or include the
More informationweb 2.0 cbna Adam Procter Technical Services Officer Monday, 19 October 2009
web 2.0 Adam Procter Technical Services Officer cbna Creative commons licence for this keynote presentation is Attribution-Noncommercial-Share Alike 3.0 Unported Recommended Reading for this lecture Trancending
More informationCSE 255 Lecture 13. Data Mining and Predictive Analytics. Triadic closure; strong & weak ties
CSE 255 Lecture 13 Data Mining and Predictive Analytics Triadic closure; strong & weak ties Monday Random models of networks: Erdos Renyi random graphs (picture from Wikipedia http://en.wikipedia.org/wiki/erd%c5%91s%e2%80%93r%c3%a9nyi_model)
More informationCSE 158 Lecture 13. Web Mining and Recommender Systems. Triadic closure; strong & weak ties
CSE 158 Lecture 13 Web Mining and Recommender Systems Triadic closure; strong & weak ties Monday Random models of networks: Erdos Renyi random graphs (picture from Wikipedia http://en.wikipedia.org/wiki/erd%c5%91s%e2%80%93r%c3%a9nyi_model)
More informationDigital Marketing Proposal
Digital Marketing Proposal ---------------------------------------------------------------------------------------------------------------------------------------------- 1 P a g e We at Tronic Solutions
More informationBehavioral Data Mining. Lecture 9 Modeling People
Behavioral Data Mining Lecture 9 Modeling People Outline Power Laws Big-5 Personality Factors Social Network Structure Power Laws Y-axis = frequency of word, X-axis = rank in decreasing order Power Laws
More informationRole of Social Media and Semantic WEB in Libraries
Role of Social Media and Semantic WEB in Libraries By Dr. Anwar us Saeed Email: anwarussaeed@yahoo.com Layout Plan Where Library streams merge the WEB Recent Evolution of the WEB Social WEB Semantic WEB
More informationTopological Tree Clustering of Social Network Search Results
Topological Tree Clustering of Social Network Search Results Richard Freeman Capgemini, Business Information Management richard.freeman@capgemini.com http://www.rfreeman.net Abstract. In the information
More informationSocial Network Mining An Introduction
Social Network Mining An Introduction Jiawei Zhang Assistant Professor Florida State University Big Data A Questionnaire Please raise your hands, if you (1) use Facebook (2) use Instagram (3) use Snapchat
More informationWeb Structure Mining Community Detection and Evaluation
Web Structure Mining Community Detection and Evaluation 1 Community Community. It is formed by individuals such that those within a group interact with each other more frequently than with those outside
More informationSocial Search Introduction to Information Retrieval INF 141/ CS 121 Donald J. Patterson
Social Search Introduction to Information Retrieval INF 141/ CS 121 Donald J. Patterson The Anatomy of a Large-Scale Social Search Engine by Horowitz, Kamvar WWW2010 Web IR Input is a query of keywords
More informationData Mining and Data Science. Dr. Laura E. Brown Rekhi 307 CS /6/15
Data Mining and Data Science Dr. Laura E. Brown Rekhi 307 CS 1000 10/6/15 My Background 2 My Background 3 My Background Engineering Pre- Med 4 My Background B.S. in Engineering with Concentration in Computer
More informationObserving the Evolution of Social Network on Weibo by Sampled Data
Observing the Evolution of Social Network on Weibo by Sampled Data Lu Ma, Gang Lu, Junxia Guo College of Information Science and Technology Beijing University of Chemical Technology Beijing, China sizheng@126.com
More informationTechnology in Action Complete, 13e (Evans et al.) Chapter 3 Using the Internet: Making the Most of the Web's Resources
Technology in Action Complete, 13e (Evans et al.) Chapter 3 Using the Internet: Making the Most of the Web's Resources 1) The Internet is. A) an internal communication system for businesses B) a communication
More informationECS 253 / MAE 253, Lecture 8 April 21, Web search and decentralized search on small-world networks
ECS 253 / MAE 253, Lecture 8 April 21, 2016 Web search and decentralized search on small-world networks Search for information Assume some resource of interest is stored at the vertices of a network: Web
More informationAlgorithms and Applications in Social Networks. 2017/2018, Semester B Slava Novgorodov
Algorithms and Applications in Social Networks 2017/2018, Semester B Slava Novgorodov 1 Lesson #1 Administrative questions Course overview Introduction to Social Networks Basic definitions Network properties
More informationAn Optimal Allocation Approach to Influence Maximization Problem on Modular Social Network. Tianyu Cao, Xindong Wu, Song Wang, Xiaohua Hu
An Optimal Allocation Approach to Influence Maximization Problem on Modular Social Network Tianyu Cao, Xindong Wu, Song Wang, Xiaohua Hu ACM SAC 2010 outline Social network Definition and properties Social
More informationOverlay (and P2P) Networks
Overlay (and P2P) Networks Part II Recap (Small World, Erdös Rényi model, Duncan Watts Model) Graph Properties Scale Free Networks Preferential Attachment Evolving Copying Navigation in Small World Samu
More informationOnline Communication. Chat Rooms Instant Messaging Blogging Social Media
Online Communication E-mail Chat Rooms Instant Messaging Blogging Social Media Advantages: Reduces cost of postage Fast and convenient Eliminates phone charges Disadvantages: May be difficult to understand
More informationFeature: Online App Builder Studio
Feature: Online App Builder Studio Beautiful Apps from Customizable Templates Deliver unique and visually stunning apps with unprecedented speed through our completely customizable templates. Start with
More informationDS504/CS586: Big Data Analytics Graph Mining Prof. Yanhua Li
Welcome to DS504/CS586: Big Data Analytics Graph Mining Prof. Yanhua Li Time: 6:00pm 8:50pm R Location: AK232 Fall 2016 Graph Data: Social Networks Facebook social graph 4-degrees of separation [Backstrom-Boldi-Rosa-Ugander-Vigna,
More informationCS224W: Analysis of Networks Jure Leskovec, Stanford University
CS224W: Analysis of Networks Jure Leskovec, Stanford University http://cs224w.stanford.edu 11/13/17 Jure Leskovec, Stanford CS224W: Analysis of Networks, http://cs224w.stanford.edu 2 Observations Models
More informationIntroduction Types of Social Network Analysis Social Networks in the Online Age Data Mining for Social Network Analysis Applications Conclusion
Introduction Types of Social Network Analysis Social Networks in the Online Age Data Mining for Social Network Analysis Applications Conclusion References Social Network Social Network Analysis Sociocentric
More informationInternet Basics. Basic Terms and Concepts. Connecting to the Internet
Internet Basics In this Learning Unit, we are going to explore the fascinating and ever-changing world of the Internet. The Internet is the largest computer network in the world, connecting more than a
More informationNon Overlapping Communities
Non Overlapping Communities Davide Mottin, Konstantina Lazaridou HassoPlattner Institute Graph Mining course Winter Semester 2016 Acknowledgements Most of this lecture is taken from: http://web.stanford.edu/class/cs224w/slides
More informationFiltering Unwanted Messages from (OSN) User Wall s Using MLT
Filtering Unwanted Messages from (OSN) User Wall s Using MLT Prof.Sarika.N.Zaware 1, Anjiri Ambadkar 2, Nishigandha Bhor 3, Shiva Mamidi 4, Chetan Patil 5 1 Department of Computer Engineering, AISSMS IOIT,
More informationSome Big Data Challenges
Some Big Data Challenges 2,500,000,000,000,000,000 Bytes (2.5 x 10 18 ) of data are created every day! (2012) or 8,000,000,000,000,000,000 (8 exabytes) of new data were stored globally by enterprises in
More informationSOCIAL MEDIA. Charles Murphy
SOCIAL MEDIA Charles Murphy Social Media Overview 1. Introduction 2. Social Media Areas Blogging Bookmarking Deals Location-based Music Photo sharing Video 3. The Fab Four FaceBook Google+ Linked In Twitter
More informationEISAS Enhanced Roadmap 2012
[Deliverable November 2012] I About ENISA The European Network and Information Security Agency (ENISA) is a centre of network and information security expertise for the EU, its Member States, the private
More informationSmall-World Models and Network Growth Models. Anastassia Semjonova Roman Tekhov
Small-World Models and Network Growth Models Anastassia Semjonova Roman Tekhov Small world 6 billion small world? 1960s Stanley Milgram Six degree of separation Small world effect Motivation Not only friends:
More informationDS504/CS586: Big Data Analytics Graph Mining Prof. Yanhua Li
Welcome to DS504/CS586: Big Data Analytics Graph Mining Prof. Yanhua Li Time: 6:00pm 8:50pm R Location: AK 233 Spring 2018 Service Providing Improve urban planning, Ease Traffic Congestion, Save Energy,
More informationJAVA - PROJECT TOPICS IEEE 2015 BASED DOMAIN S.NO TOPIC CODE
CLOUD COMPUTING WEB-SERVICES BASED DATABASE: SQL, MY-SQL, ORACLE JAVA - PROJECT TOPICS IEEE 2015 BASED 1 A Hybrid Cloud Approach for Secure Authorized Deduplication JCC1501 2 A Secure and Dynamic Multi-keyword
More informationOpen Research Online The Open University s repository of research publications and other research outputs
Open Research Online The Open University s repository of research publications and other research outputs Social Web Communities Conference or Workshop Item How to cite: Alani, Harith; Staab, Steffen and
More informationMining and Analyzing Online Social Networks
The 5th EuroSys Doctoral Workshop (EuroDW 2011) Salzburg, Austria, Sunday 10 April 2011 Mining and Analyzing Online Social Networks Emilio Ferrara eferrara@unime.it Advisor: Prof. Giacomo Fiumara PhD School
More informationbeyond social networks
beyond social networks Small world phenomenon: high clustering C network >> C random graph low average shortest path l network ln( N)! neural network of C. elegans,! semantic networks of languages,! actor
More informationSIPCache: A Distributed SIP Location Service for Mobile Ad-Hoc Networks
SIPCache: A Distributed SIP Location Service for Mobile Ad-Hoc Networks Simone Leggio Hugo Miranda Kimmo Raatikainen Luís Rodrigues University of Helsinki University of Lisbon August 16, 2006 Abstract
More informationHTML 5 and CSS 3, Illustrated Complete. Unit M: Integrating Social Media Tools
HTML 5 and CSS 3, Illustrated Complete Unit M: Integrating Social Media Tools Objectives Understand social networking Integrate a Facebook account with a Web site Integrate a Twitter account feed Add a
More informationJure Leskovec Machine Learning Department Carnegie Mellon University
Jure Leskovec Machine Learning Department Carnegie Mellon University Currently: Soon: Today: Large on line systems have detailed records of human activity On line communities: Facebook (64 million users,
More informationRANDOM-REAL NETWORKS
RANDOM-REAL NETWORKS 1 Random networks: model A random graph is a graph of N nodes where each pair of nodes is connected by probability p: G(N,p) Random networks: model p=1/6 N=12 L=8 L=10 L=7 The number
More informationAn Exploratory Journey Into Network Analysis A Gentle Introduction to Network Science and Graph Visualization
An Exploratory Journey Into Network Analysis A Gentle Introduction to Network Science and Graph Visualization Pedro Ribeiro (DCC/FCUP & CRACS/INESC-TEC) Part 1 Motivation and emergence of Network Science
More informationPorting Social Media Contributions with SIOC
Porting Social Media Contributions with SIOC Uldis Bojars, John G. Breslin, and Stefan Decker DERI, National University of Ireland, Galway, Ireland firstname.lastname@deri.org Abstract. Social media sites,
More informationThe Social Grid. Leveraging the Power of the Web and Focusing on Development Simplicity
The Social Grid Leveraging the Power of the Web and Focusing on Development Simplicity Tony Hey Corporate Vice President of Technical Computing at Microsoft TCP/IP versus ISO Protocols ISO Committees disconnected
More informationThe Role of Homophily and Popularity in Informed Decentralized Search
The Role of Homophily and Popularity in Informed Decentralized Search Florian Geigl and Denis Helic Knowledge Technologies Institute In eldgasse 13/5. floor, 8010 Graz, Austria {florian.geigl,dhelic}@tugraz.at
More informationLesson 2: Internet Communication
Lesson 2: Internet Communication Lesson 2 Objectives Define modern Web technologies Define social networking Define and use instant messaging and text messaging Use Windows Remote Assistance Discuss blogging
More informationSocial Media Tools. March 13, 2010 Presented by: Noble Studios, Inc.
March 13, 2010 Presented by: Noble Studios, Inc. 1 Communication Timeline 2 Familiar Social Media Sites According to Facebook, more than 1.5 million local businesses have active pages on Facebook According
More informationCSE 258 Lecture 12. Web Mining and Recommender Systems. Social networks
CSE 258 Lecture 12 Web Mining and Recommender Systems Social networks Social networks We ve already seen networks (a little bit) in week 3 i.e., we ve studied inference problems defined on graphs, and
More informationSocial Networking: Managing the Risks and Realizing the Benefits
Social Networking: Managing the Risks and Realizing the Benefits 11 th Annual Compliance & Ethics Institute Las Vegas, NV Jim Donaldson, M.S., MPA, CHC, CISSP, CIPP/US Director of Compliance, Privacy and
More informationStudying the Properties of Complex Network Crawled Using MFC
Studying the Properties of Complex Network Crawled Using MFC Varnica 1, Mini Singh Ahuja 2 1 M.Tech(CSE), Department of Computer Science and Engineering, GNDU Regional Campus, Gurdaspur, Punjab, India
More informationChapter 1 Living in a Network Centric World
Chapter 1 Living in a Network Centric World Introduction The globalization of the Internet has succeeded faster than anyone could have imagined. The manner in which social, commercial, political and personal
More informationISRAEL NATIONAL CYBER SECURITY STRATEGY IN BRIEF
SEPTEMBER 2017 ISRAEL NATIONAL CYBER SECURITY STRATEGY IN BRIEF STATE OF ISRAEL PRIME MINISTER S OFFICE NATIONAL CYBER DIRECTORATE Vision and Objective 5 Development of Israel s national cyber security
More informationA B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Glossary A B C D E F G H I J K L M N O P Q R S T U V W X Y Z A App See Application Application An application (sometimes known as an app ) is a computer program which allows the user to perform a specific
More informationUNIT-V WEB MINING. 3/18/2012 Prof. Asha Ambhaikar, RCET Bhilai.
UNIT-V WEB MINING 1 Mining the World-Wide Web 2 What is Web Mining? Discovering useful information from the World-Wide Web and its usage patterns. 3 Web search engines Index-based: search the Web, index
More informationTELSTRA TECH SAVVY SENIORS - BEGINNERS GUIDE INTRODUCTION TO SOCIAL MEDIA - PART 1
TOPIC: INTRODUCTION TO SOCIAL MEDIA PART 1 WHAT TO USE AND WHEN The internet helps you stay in touch with friends and loved ones. Social media sites like Facebook and Twitter let you see what s happening
More informationJure Leskovec Computer Science Department Cornell University / Stanford University
Jure Leskovec Computer Science Department Cornell University / Stanford University Large on line systems have detailed records of human activity On line communities: Facebook (64 million users, billion
More informationCS224W Project Write-up Static Crawling on Social Graph Chantat Eksombatchai Norases Vesdapunt Phumchanit Watanaprakornkul
1 CS224W Project Write-up Static Crawling on Social Graph Chantat Eksombatchai Norases Vesdapunt Phumchanit Watanaprakornkul Introduction Our problem is crawling a static social graph (snapshot). Given
More informationWeb 2.0 Tutorial. Jacek Kopecký STI Innsbruck
Web 2.0 Tutorial Jacek Kopecký STI Innsbruck SOA4All Kick-off -Madrid, 25th-27th March 2008 Web 2.0 and SOA: Overview Questions to be addressed: What is Web 2.0? What technologies does Web 2.0 comprise?
More informationInternet Applications. Q. What is Internet Explorer? Explain features of Internet Explorer.
Internet Applications Q. What is Internet Explorer? Explain features of Internet Explorer. Internet explorer: Microsoft Internet Explorer is a computer program called a browser that helps you interact
More informationOpen Federated Social Networks Oscar Rodríguez Rocha
Open Federated Social Networks Oscar Rodríguez Rocha 178691 Federated document database Documents are stored on different servers Access through browsers Any individual, company, or organization can own
More informationMEMA. Memory Management for Museum Exhibitions. Independent Study Report 2970 Fall 2011
MEMA Memory Management for Museum Exhibitions Independent Study Report 2970 Fall 2011 Author: Xiaoning Bai Yuanyuan Ye Supervisors: Dr. Peter Brusilovsky, Yiling Lin Part I. Introduction to MEMA MEMA is
More informationRandom Generation of the Social Network with Several Communities
Communications of the Korean Statistical Society 2011, Vol. 18, No. 5, 595 601 DOI: http://dx.doi.org/10.5351/ckss.2011.18.5.595 Random Generation of the Social Network with Several Communities Myung-Hoe
More informationIntroduction to Text Mining. Hongning Wang
Introduction to Text Mining Hongning Wang CS@UVa Who Am I? Hongning Wang Assistant professor in CS@UVa since August 2014 Research areas Information retrieval Data mining Machine learning CS@UVa CS6501:
More informationWHAT IS GOOGLE+ AND WHY SHOULD I USE IT?
CHAPTER ONE WHAT IS GOOGLE+ AND WHY SHOULD I USE IT? In this chapter: + Discovering Why Google+ Is So Great + What Is the Difference between Google+ and Other Social Networks? + Does It Cost Money to Use
More informationGetting Started with Memcached. Ahmed Soliman
Getting Started with Memcached Ahmed Soliman In this package, you will find: A Biography of the author of the book A synopsis of the book s content Information on where to buy this book About the Author
More informationOnline Communication. Chat Rooms Instant Messaging Blogging Social Media
Online Communication E-mail Chat Rooms Instant Messaging Blogging Social Media { Advantages: { Reduces cost of postage Fast and convenient Need an email address to sign up for other online accounts. Eliminates
More informationA Meta Social Networking Approach Towards Decentralization
A Meta Social Networking Approach Towards Decentralization Pili Hu and Wing Cheong Lau Information Engieering Department Chinese University of Hong Kong Email: {hupili,wclau}@ie.cuhk.edu.hk Abstract There
More informationTechnology In Action, Complete, 14e (Evans et al.) Chapter 3 Using the Internet: Making the Most of the Web's Resources
Technology In Action, Complete, 14e (Evans et al.) Chapter 3 Using the Internet: Making the Most of the Web's Resources 1) The Internet is. A) an internal communication system for businesses B) a communication
More informationNavigation in Networks. Networked Life NETS 112 Fall 2017 Prof. Michael Kearns
Navigation in Networks Networked Life NETS 112 Fall 2017 Prof. Michael Kearns The Navigation Problem You are an individual (vertex) in a very large social network You want to find a (short) chain of friendships
More informationPart 3: Online Social Networks
1 Part 3: Online Social Networks Today's plan Project 2 Questions? 2 Social networking services Social communities Bebo, MySpace, Facebook, etc. Content sharing YouTube, Flickr, MSN Soapbox, etc. Corporate
More informationemergency communication strategies part 2: getting the word out
emergency communication strategies part 2: getting the word out Carol Spencer (@CarolSpencerNJ) Digital & Social Media Consultant at Stormzero LLC former Finance Director at National Association of Government
More informationDavid Easley and Jon Kleinberg January 24, 2007
Networks: Spring 2007 Graph Theory David Easley and Jon Kleinberg January 24, 2007 A graph is simply a way of encoding the pairwise relationships among a set of objects: we will refer to the objects as
More informationGraph and Link Mining
Graph and Link Mining Graphs - Basics A graph is a powerful abstraction for modeling entities and their pairwise relationships. G = (V,E) Set of nodes V = v,, v 5 Set of edges E = { v, v 2, v 4, v 5 }
More informationHow to explore big networks? Question: Perform a random walk on G. What is the average node degree among visited nodes, if avg degree in G is 200?
How to explore big networks? Question: Perform a random walk on G. What is the average node degree among visited nodes, if avg degree in G is 200? Questions from last time Avg. FB degree is 200 (suppose).
More informationAn Introduction to Search Engines and Web Navigation
An Introduction to Search Engines and Web Navigation MARK LEVENE ADDISON-WESLEY Ал imprint of Pearson Education Harlow, England London New York Boston San Francisco Toronto Sydney Tokyo Singapore Hong
More informationCheryl Bledsoe, EM Division Manager Clark Regional Emergency Services Agency (CRESA)
Cheryl Bledsoe, EM Division Manager Clark Regional Emergency Services Agency (CRESA) WHO? Cheryl Bledsoe, Sociologist & Trend Watcher 10 years background in Criminal Justice WHAT? Emergency Manager w/no
More informationComplex Networks. Structure and Dynamics
Complex Networks Structure and Dynamics Ying-Cheng Lai Department of Mathematics and Statistics Department of Electrical Engineering Arizona State University Collaborators! Adilson E. Motter, now at Max-Planck
More informationCS 224W Final Report Group 37
1 Introduction CS 224W Final Report Group 37 Aaron B. Adcock Milinda Lakkam Justin Meyer Much of the current research is being done on social networks, where the cost of an edge is almost nothing; the
More informationINFORMATION COMUNICATION TECHNOLOGY SKS 1362
INFORMATION COMUNICATION TECHNOLOGY SKS 1362 Lecture Six http://www.dr-qais.com Facebook: Dr Prince Badakhshi 1 2 It is a kind of business that buying and selling of products or services are conducted
More informationMAE 298, Lecture 9 April 30, Web search and decentralized search on small-worlds
MAE 298, Lecture 9 April 30, 2007 Web search and decentralized search on small-worlds Search for information Assume some resource of interest is stored at the vertices of a network: Web pages Files in
More informationNetwork Thinking. Complexity: A Guided Tour, Chapters 15-16
Network Thinking Complexity: A Guided Tour, Chapters 15-16 Neural Network (C. Elegans) http://gephi.org/wp-content/uploads/2008/12/screenshot-celegans.png Food Web http://1.bp.blogspot.com/_vifbm3t8bou/sbhzqbchiei/aaaaaaaaaxk/rsc-pj45avc/
More informationChapter 3. E-commerce The Evolution of the Internet 1961 Present. The Internet: Technology Background. The Internet: Key Technology Concepts
E-commerce 2015 business. technology. society. eleventh edition Kenneth C. Laudon Carol Guercio Traver Chapter 3 E-commerce Infrastructure: The Internet, Web, and Mobile Platform Copyright 2015 Pearson
More informationSolace JMS Broker Delivers Highest Throughput for Persistent and Non-Persistent Delivery
Solace JMS Broker Delivers Highest Throughput for Persistent and Non-Persistent Delivery Java Message Service (JMS) is a standardized messaging interface that has become a pervasive part of the IT landscape
More informationIntroducing IBM Lotus Sametime 7.5 software.
Real-time collaboration solutions March 2006 Introducing IBM Lotus Sametime 7.5 software. Adam Gartenberg Offering Manager, Real-time and Team Collaboration Page 2 Contents 2 Introduction 3 Enhanced instant
More informationMachine-Based Penetration Testing
Always in Control CyBot Suite Machine-Based Penetration Testing CyBot PRODUCT SUITE Unique, patented Machine-based Penetration Testing Software with Global Attack Path Scenarios (APS) product suite: CyBot
More informationLesson 17: Your Life Online
Living Online Lesson 17: Your Life Online Lesson Objectives In this lesson, you will learn about your online identity and how to protect it. You will also learn about the adverse effects prolonged computing
More informationSummary of Consultation with Key Stakeholders
Summary of Consultation with Key Stakeholders Technology and Communications Sector Electronic Manufacturing Services & Original Design Manufacturing Software & IT Services Hardware Semiconductors Telecommunication
More informationHeuristics for the Critical Node Detection Problem in Large Complex Networks
Heuristics for the Critical Node Detection Problem in Large Complex Networks Mahmood Edalatmanesh Department of Computer Science Submitted in partial fulfilment of the requirements for the degree of Master
More informationDigital Marketing Communication Award
BIGROCKDESIGNS computer training consultants learn@bigrockdesigns.com ' www.bigrockdesigns.com Digital Marketing Communication Award Course Outline Our Digital Marketing Communication Award course encompasses
More information