Measurement and Analysis of. Alan Mislove, Massimiliano Marcon,KrishnaP.Gummadi, Peter Druschel, Bobby Bhattacharjee
|
|
- Theodore Hamilton
- 7 years ago
- Views:
Transcription
1 Measurement and Analysis of Online Social Networks Alan Mislove, Massimiliano Marcon,KrishnaP.Gummadi, Peter Druschel, Bobby Bhattacharjee
2 Background Flickr, YouTube, LiveJournal and Orkut. Rely on an explicit user graph to organize locate and share content as well as contacts. Three main factors, Users, Links and Groups.
3 Motivation Help deliberate attempts of manipulation(efficient algorithms to infer the actual degree of shared interest or the reliability of user). Understand the impact of robustness and security of distributed online social networks to the future internet. Impact on other disciplines(social science, online marketing )
4 Related Work Social network Milgram(6 hops), Small world effect, strong and weak tie. Information network SCC(stronly connected component) Complex network theory Power law networks, Scale-free network, Small-world network.
5 Methodology Crawl the user graphs by accessing the public web interface provided by the sites. Able to access to large data sets from multiple sites. Focus on the large weakly connected component(wcc).
6 Challenges in crawling large graphs Crawling the entire connected component. (Crawling entire connection component is not feasible, and small sample set produced biased sample of nodes.) Using only forward links.(only forward links does not necessarily crawl entire WCC)
7 Figure 1
8 Crawling social networks 1. Flickr Photo sharing site based on a social network. Only allows to query forward links. Among 6,902 sample users, 1,859(26%) being discovered during crawl. BFS to rest 5,043 users, only 250(5%) could reach crawled set and were definitively in the WCC.
9 Explore the social network using the remaining missing nodes as seed, only 11,468 new nodes that were not in the connected component of 1.8 million nodes discovered in the original crawl. Inside it, 44.8% no forward links, 29.3% one link, 5.4% two or three links. 20.3% has four or more. It shows the missing nodes tend to have low degree and connected only to small unreachable clusters from the crawl component. Conclusion: the crawl of the large WCC covers a large fraction of the users who are part of WCC
10 2.LiveJournal. Popular blogging sites. Data set covers 5.3 million user and 72 million links. The fraction not reachable by using only forward links is only 7.64%. And in 404,134 possibly missing users, 49.9% has single forward link, 21% has two or three links. 19.4% has four more.
11 3.Orkut Links undirected and link creation requires consent from the target. 11.3% subset as sample dataset, is likely to be similar to other crawls of similar size. Partial BFS crawls will overestimate the average node degree and underestimate the level of symmetry.
12 4. YouTube Video sharing site includes a social network. Query only in the forward direction. No API for export group information. Group information obtained by screenscraping the HTML page attached to user profile. No way to know the number of absent users.
13 Summary The Flickrand YouTube data sets may not contain some of the nodes in the large WCC, but this fraction is likely to be very small. The LiveJournaldata set covers almost the complete population of LiveJournal, and contains the entire large WCC. The Orkutdata set represents a modest portion of the network, and is subject to the sampling bias resulting from a partial BFS crawl.
14 High-level statistics
15 Analysis of network structure 1. Link symmetry Pages with high indegree tend to be authorities. Symmetry increases the overall connectivity of the network and reduces its diameter Symmetry can also make it harder to identify reputable sources of information just by analyzing the network structure, because reputed sources tend to dilute their importance when pointing back to arbitrary users who link to them.
16 2. Power-law node degrees. All of the networks show behavior consistent with a power-law degree distribution.
17 Tested stability. In and out degree power-law exponents shown to differ significantly.
18 Distruibution of incomming and outgoing links over nodes in the web and Flickr graphs.
19 3. Correlation of indegree and outdegree Node with high outdegree also tend to have high indegree.
20 Outdegree to indegree ratio. Can be explained by the high number of symmetric links.
21 4. Path lengths and diameter. Social networks have significantly shorter average path lengths and diameters.
22 5. Link degree correlations. 5.1 Joint degree distribution. The JDD is approximated by the degree correlation function k nn, and increasing k nn indicates a tendency of higher-degree nodes to connect to other high-degree nodes. A decreasing k nn represent the opposite.
23 Figure 6 The exception on YouTube may due to a few extremely popular users on YouTube. Bump at Orkut curve is likely due to the undersampling of users.
24 5.2 Scale-free behavior Scale-free metric of the networks are shown in the legend of Figure 6. A high scale-free metric means that highdegree nodes tend to connect to other highdegree nodes, while a low scale-free metric means that highdegree nodes tend to connect to low-degree nodes.
25 5.3 Assortativity The scale-free metric is related to the assortativitycoefficient r, which is a measure of the likelihood for nodes to connect to other nodes with similar degrees. The assortativity coefficient ranges between -1 and 1; a high assortativitycoefficient means that nodes tend to connect to nodes of similardegree, while a negative coefficient means that nodes likely connect to nodes with very different degree from their own.
26 Calculate the assortativitycoefficients for each of the networks. Flickr LiveJournal Orkut YouTube Web Internet 0.189
27 6. Densely connected Core. Define a core of a network as any (minimal) set of nodes that satisfies two properties: First, the core must be necessary for the connectivity of the network (i.e., removing the core breaks the remainder of the nodes into many small, disconnected clusters). Second, the core must be strongly connected with a relatively small diameter. Thus, a core is a small group of wellconnected group of nodes that is necessary to keep the remainder of the network connected.
28 Remove increasing numbers of the highest degree nodes and analyze the connectivity of the remaining graph.
29 Path length increase as larger subgraphs of the core generated (progressively including nodes ordered inversely by degree).
30 The graphs studied have a densely connected core comprising of between 1% and 10% of the highest degree nodes.
31 7. Tightly clustered fringe. The clustering coefficient of a node with N neighbors is defined as the number of directed links that exist between the node s N neighbors, divided by the number of possible directed links that could exist between the node s neighbors (N(N 1)). The clustering coefficient of a graph is the average clustering coefficient of all its nodes, and we denote it as C.
32 Table 4 Unusually high clustering coefficient suggests the presence of strong local clustering. People tends to be introduced in by friends.
33 Clustering coefficient Outdegree The clustering coefficient is higher for nodes of low degree. (small world network, scale- free)
34 8. Groups. Group size follows power-law. Present tightly clustered communities of users in the social network.
35 Members of smaller user groups tend to be more clustered than those of large groups.
36 User participation in groups varies with out degrees. Low degree node -> very few communities High degree node -> multiple groups
37 Summary: The degree distributions in social networks follow a power-law, and the power-law coefficients for both in-degree and outdegreeare similar. Social networks appear to be composed of a large number of highly connected clusters consisting of relatively low-degree nodes. The networks each contain a large, densely connected core. (10% nodes with highest degree)
38 Discussion 1. Information dessemination and Search Simple unstructured search algorithms could be designed if the core users were to store some state about other users. ( supernodes )
39 2. Trust If tight core is hacked by malicious users, they will easily skew trust paths.(trust inference algorithms.) Users will not be highly trusted unless they form direct links to other users.(gradually pulled in to the core.)
40 Temporal invariance. Repeated crawl carried out on both Flickr and YoTube on May 2007, and result are still valid. Even though the networks are growing rapidly, their basic structure is not changing significantly.
41 Conclusion Social networks have a much higher fraction of symmetric links and also exhibit much higher levels of local clustering
Measurement and Analysis of Online Social Networks
Measurement and Analysis of Online Social Networks Alan Mislove Massimiliano Marcon Krishna P. Gummadi Peter Druschel Bobby Bhattacharjee Max Planck Institute for Software Systems Rice University University
More informationBeyond Social Graphs: User Interactions in Online Social Networks and their Implications
Beyond Social Graphs: User Interactions in Online Social Networks and their Implications CHRISTO WILSON, ALESSANDRA SALA, KRISHNA P. N. PUTTASWAMY, and BEN Y. ZHAO, University of California Santa Barbara
More informationBig 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 informationOnline Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
RICE UNIVERSITY Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems by Alan E. Mislove A Thesis Submitted in Partial Fulfillment of the Requirements for the
More informationUser Interactions in Social Networks and their Implications
User Interactions in Social Networks and their Implications Christo Wilson, Bryce Boe, Alessandra Sala, Krishna P. N. Puttaswamy, and Ben Y. Zhao Computer Science Department, University of California at
More informationA discussion of Statistical Mechanics of Complex Networks P. Part I
A discussion of Statistical Mechanics of Complex Networks Part I Review of Modern Physics, Vol. 74, 2002 Small Word Networks Clustering Coefficient Scale-Free Networks Erdös-Rényi model cover only parts
More informationA Measurement-driven Analysis of Information Propagation in the Flickr Social Network
A Measurement-driven Analysis of Information Propagation in the Flickr Social Network Meeyoung Cha MPI-SWS Campus E1 4 Saarbrücken, Germany mcha@mpi-sws.org Alan Mislove MPI-SWS Campus E1 4 Saarbrücken,
More informationGreedy Routing on Hidden Metric Spaces as a Foundation of Scalable Routing Architectures
Greedy Routing on Hidden Metric Spaces as a Foundation of Scalable Routing Architectures Dmitri Krioukov, kc claffy, and Kevin Fall CAIDA/UCSD, and Intel Research, Berkeley Problem High-level Routing is
More informationGraphs over Time Densification Laws, Shrinking Diameters and Possible Explanations
Graphs over Time Densification Laws, Shrinking Diameters and Possible Explanations Jurij Leskovec, CMU Jon Kleinberg, Cornell Christos Faloutsos, CMU 1 Introduction What can we do with graphs? What patterns
More informationSome questions... Graphs
Uni Innsbruck Informatik - 1 Uni Innsbruck Informatik - 2 Some questions... Peer-to to-peer Systems Analysis of unstructured P2P systems How scalable is Gnutella? How robust is Gnutella? Why does FreeNet
More informationDECENTRALIZED SCALE-FREE NETWORK CONSTRUCTION AND LOAD BALANCING IN MASSIVE MULTIUSER VIRTUAL ENVIRONMENTS
DECENTRALIZED SCALE-FREE NETWORK CONSTRUCTION AND LOAD BALANCING IN MASSIVE MULTIUSER VIRTUAL ENVIRONMENTS Markus Esch, Eric Tobias - University of Luxembourg MOTIVATION HyperVerse project Massive Multiuser
More informationOverview of the Stateof-the-Art. Networks. Evolution of social network studies
Overview of the Stateof-the-Art in Social Networks INF5370 spring 2013 Evolution of social network studies 1950-1970: mathematical studies of networks formed by the actual human interactions Pandemics,
More informationThe ebay Graph: How Do Online Auction Users Interact?
The ebay Graph: How Do Online Auction Users Interact? Yordanos Beyene, Michalis Faloutsos University of California, Riverside {yordanos, michalis}@cs.ucr.edu Duen Horng (Polo) Chau, Christos Faloutsos
More informationA Network Science Perspective of a Distributed Reputation Mechanism
A Network Science Perspective of a Distributed Reputation Mechanism Rahim Delaviz, Niels Zeilemaker, Johan A. Pouwelse, and Dick H.J. Epema Delft University of Technology, the Netherlands Email: j.a.pouwelse@tudelft.nl
More informationGeneral Network Analysis: Graph-theoretic. COMP572 Fall 2009
General Network Analysis: Graph-theoretic Techniques COMP572 Fall 2009 Networks (aka Graphs) A network is a set of vertices, or nodes, and edges that connect pairs of vertices Example: a network with 5
More informationAre Friends Overrated? A Study for the Social Aggregator Digg.com
Are Friends Overrated? A Study for the Social Aggregator Digg.com Christian Doerr, Siyu Tang, Norbert Blenn, Piet Van Mieghem Department of Telecommunication TU Delft, Mekelweg 4, 2628CD Delft, The Netherlands
More informationAnalysis of Topological Characteristics of Huge Online Social Networking Services
WWW 2007 / Trac: Semantic Web Session: Semantic Web and Web 2.0 Analysis of Topological Characteristics of Huge Online Social Networing Services Yong Yeol Ahn Department of Physics KAIST, Deajeon, Korea
More informationDo All Birds Tweet the Same? Characterizing Twitter Around the World
Do All Birds Tweet the Same? Characterizing Twitter Around the World Barbara Poblete 1,2 Ruth Garcia 3,4 Marcelo Mendoza 5,2 Alejandro Jaimes 3 {bpoblete,ruthgavi,mendozam,ajaimes}@yahoo-inc.com 1 Department
More informationSocial Network Mining
Social Network Mining Data Mining November 11, 2013 Frank Takes (ftakes@liacs.nl) LIACS, Universiteit Leiden Overview Social Network Analysis Graph Mining Online Social Networks Friendship Graph Semantics
More informationStrong 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 informationType Specification Based Video Spam Detection and Prevention
Type Specification Based Video Spam Detection and Prevention Pooja Kamboj 1, Asst Chandna Jain 2 M.Tech, CSE, JCDM College of Engineering, Sirsa, India 1 Asst Professor, CSE, JCDM College of Engineering,
More informationSocial 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 informationSocial Media Mining. Network Measures
Klout Measures and Metrics 22 Why Do We Need Measures? Who are the central figures (influential individuals) in the network? What interaction patterns are common in friends? Who are the like-minded users
More informationGoogle+ or Google-? Dissecting the Evolution of the New OSN in its First Year
Google+ or Google-? Dissecting the Evolution of the New OSN in its First Year Roberto Gonzalez, Ruben Cuevas Universidad Carlos III de Madrid {rgonza1,rcuevas}@it.uc3m.es Reza Motamedi, Reza Rejaie University
More information3.3 Patterns of infrequent interactions 3. PAIRWISE USER INTERACTIONS. 3.1 Data used. 3.2 Distribution of the number of wall posts
On the Evolution of User Interaction in Facebook Bimal Viswanath Alan Mislove Meeyoung Cha Krishna P. Gummadi Max Planck Institute for Software Systems (MPI-SWS) Rice University Kaiserslautern/Saarbrücken,
More informationAn 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 informationThe Joint Degree Distribution as a Definitive Metric of the Internet AS-level Topologies
The Joint Degree Distribution as a Definitive Metric of the Internet AS-level Topologies Priya Mahadevan, Dimitri Krioukov, Marina Fomenkov, Brad Huffaker, Xenofontas Dimitropoulos, kc claffy, Amin Vahdat
More informationSocial Network Analysis
Social Network Analysis Challenges in Computer Science April 1, 2014 Frank Takes (ftakes@liacs.nl) LIACS, Leiden University Overview Context Social Network Analysis Online Social Networks Friendship Graph
More informationGraph 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 informationCharacterizing Unstructured Overlay Topologies in Modern P2P File-Sharing Systems
Characterizing Unstructured Overlay Topologies in Modern P2P File-Sharing Systems Daniel Stutzbach, Reza Rejaie University of Oregon {agthorr,reza}@cs.uoregon.edu Subhabrata Sen AT&T Labs Research sen@research.att.com
More informationUSING SPECTRAL RADIUS RATIO FOR NODE DEGREE TO ANALYZE THE EVOLUTION OF SCALE- FREE NETWORKS AND SMALL-WORLD NETWORKS
USING SPECTRAL RADIUS RATIO FOR NODE DEGREE TO ANALYZE THE EVOLUTION OF SCALE- FREE NETWORKS AND SMALL-WORLD NETWORKS Natarajan Meghanathan Jackson State University, 1400 Lynch St, Jackson, MS, USA natarajan.meghanathan@jsums.edu
More informationPractical Graph Mining with R. 5. Link Analysis
Practical Graph Mining with R 5. Link Analysis Outline Link Analysis Concepts Metrics for Analyzing Networks PageRank HITS Link Prediction 2 Link Analysis Concepts Link A relationship between two entities
More informationUnderstanding Latent Interactions in Online Social Networks
Understanding Latent Interactions in Online Social Networks Jing Jiang,ChristoWilson,XiaoWang,PengHuang,WenpengSha, Yafei Dai and Ben Y. Zhao Peking University, Beijing, China U. C. Santa Barbara, Santa
More informationPeer Production of Online Learning Resources: A Social Network Analysis
Peer Production of Online Learning Resources: A Social Network Analysis Beijie Xu and Mimi M. Recker beijie.xu@aggiemail.usu.edu, mimi.recker@usu.edu Department of Instructional Technology & Learning Sciences,
More informationThe Spread of Media Content Through Blogs
Noname manuscript No. (will be inserted by the editor) The Spread of Media Content Through Blogs Meeyoung Cha Juan Antonio Navarro Pérez Hamed Haddadi Received: date / Accepted: date Abstract Blogs are
More informationNetwork Analysis. BCH 5101: Analysis of -Omics Data 1/34
Network Analysis BCH 5101: Analysis of -Omics Data 1/34 Network Analysis Graphs as a representation of networks Examples of genome-scale graphs Statistical properties of genome-scale graphs The search
More informationExtracting Information from Social Networks
Extracting Information from Social Networks Aggregating site information to get trends 1 Not limited to social networks Examples Google search logs: flu outbreaks We Feel Fine Bullying 2 Bullying Xu, Jun,
More informationUnderstanding 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! E6893 Big Data Analytics Lecture 10:! Linked Big Data Graph Computing (II)
E6893 Big Data Analytics Lecture 10: Linked Big Data Graph Computing (II) Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science Mgr., Dept. of Network Science and
More informationPhysical Theories of the Evolution of Online Social Networks: A Discussion Impulse
Physical Theories of the Evolution of Online Social Networks: A Discussion Impulse Lutz Poessneck, Henning Hofmann, Ricardo Buettner FOM Hochschule fuer Oekonomie & Management, University of Applied Sciences
More informationBioinformatics: Network Analysis
Bioinformatics: Network Analysis Graph-theoretic Properties of Biological Networks COMP 572 (BIOS 572 / BIOE 564) - Fall 2013 Luay Nakhleh, Rice University 1 Outline Architectural features Motifs, modules,
More informationDo Social Networks Improve e-commerce? A Study on Social Marketplaces
Do Social Networks Improve e-commerce? A Study on Social Marketplaces Gayatri Swamynathan, Christo Wilson, Bryce Boe, Kevin Almeroth, and Ben Y. Zhao Department of Computer Science, UC Santa Barbara, CA
More informationInformation Network or Social Network? The Structure of the Twitter Follow Graph
Information Network or Social Network? The Structure of the Twitter Follow Graph Seth A. Myers, Aneesh Sharma, Pankaj Gupta, and Jimmy Lin Twitter, Inc. @seth_a_myers @aneeshs @pankaj @lintool ABSTRACT
More informationPrivacy Settings from Contextual Attributes: A Case Study Using Google Buzz
Privacy Settings from Contextual Attributes: A Case Study Using Google Buzz Daisuke Mashima Georgia Institute of Technology mashima@cc.gatech.edu Prateek Sarkar Google Inc. prateeks@google.com Elaine Shi
More informationSmall-World Characteristics of Internet Topologies and Implications on Multicast Scaling
Small-World Characteristics of Internet Topologies and Implications on Multicast Scaling Shudong Jin Department of Electrical Engineering and Computer Science, Case Western Reserve University Cleveland,
More informationGraph Mining and Social Network Analysis
Graph Mining and Social Network Analysis Data Mining and Text Mining (UIC 583 @ Politecnico di Milano) References Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", The Morgan Kaufmann
More informationImputation of missing network data: Some simple procedures
Imputation of missing network data: Some simple procedures Mark Huisman Dept. of Psychology University of Groningen Abstract Analysis of social network data is often hampered by non-response and missing
More informationGraph Theory and Complex Networks: An Introduction. Chapter 06: Network analysis. Contents. Introduction. Maarten van Steen. Version: April 28, 2014
Graph Theory and Complex Networks: An Introduction Maarten van Steen VU Amsterdam, Dept. Computer Science Room R.0, steen@cs.vu.nl Chapter 0: Version: April 8, 0 / Contents Chapter Description 0: Introduction
More informationChapter 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 informationCourse on Social Network Analysis Graphs and Networks
Course on Social Network Analysis Graphs and Networks Vladimir Batagelj University of Ljubljana Slovenia V. Batagelj: Social Network Analysis / Graphs and Networks 1 Outline 1 Graph...............................
More informationStructural constraints in complex networks
Structural constraints in complex networks Dr. Shi Zhou Lecturer of University College London Royal Academy of Engineering / EPSRC Research Fellow Part 1. Complex networks and three key topological properties
More informationGENERATING AN ASSORTATIVE NETWORK WITH A GIVEN DEGREE DISTRIBUTION
International Journal of Bifurcation and Chaos, Vol. 18, o. 11 (2008) 3495 3502 c World Scientific Publishing Company GEERATIG A ASSORTATIVE ETWORK WITH A GIVE DEGREE DISTRIBUTIO JI ZHOU, XIAOKE XU, JIE
More informationAn 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 informationLooking at the Blogosphere Topology through Different Lenses
Looking at the Blogosphere Topology through Different Lenses Xiaolin Shi Dept. of Electrical Engineering and Computer Science University of Michigan Ann Arbor, MI 48109 shixl@umich.edu Belle Tseng NEC
More informationHow To Understand The Network Of A Network
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 informationLesson 4 Measures of Central Tendency
Outline Measures of a distribution s shape -modality and skewness -the normal distribution Measures of central tendency -mean, median, and mode Skewness and Central Tendency Lesson 4 Measures of Central
More informationNetwork Analysis For Sustainability Management
Network Analysis For Sustainability Management 1 Cátia Vaz 1º Summer Course in E4SD Outline Motivation Networks representation Structural network analysis Behavior network analysis 2 Networks Over the
More informationStatistics and Social Network of YouTube Videos
Statistics and Social Network of YouTube Videos Xu Cheng Cameron Dale Jiangchuan Liu School of Computing Science Simon Fraser University Burnaby, BC, Canada Email: {xuc, camerond, jcliu}@cs.sfu.ca Abstract
More informationEvolution of a Location-based Online Social Network: Analysis and Models
Evolution of a Location-based Online Social Network: Analysis and Models Miltiadis Allamanis Computer Laboratory University of Cambridge ma536@cam.ac.uk Salvatore Scellato Computer Laboratory University
More informationGraph theoretic approach to analyze amino acid network
Int. J. Adv. Appl. Math. and Mech. 2(3) (2015) 31-37 (ISSN: 2347-2529) Journal homepage: www.ijaamm.com International Journal of Advances in Applied Mathematics and Mechanics Graph theoretic approach to
More informationFacebook 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 informationGraph models for the Web and the Internet. Elias Koutsoupias University of Athens and UCLA. Crete, July 2003
Graph models for the Web and the Internet Elias Koutsoupias University of Athens and UCLA Crete, July 2003 Outline of the lecture Small world phenomenon The shape of the Web graph Searching and navigation
More informationWhy Rumors Spread Fast in Social Networks
Why Rumors Spread Fast in Social Networks Benjamin Doerr 1, Mahmoud Fouz 2, and Tobias Friedrich 1,2 1 Max-Planck-Institut für Informatik, Saarbrücken, Germany 2 Universität des Saarlandes, Saarbrücken,
More informationCrawling and Detecting Community Structure in Online Social Networks using Local Information
Crawling and Detecting Community Structure in Online Social Networks using Local Information TU Delft - Network Architectures and Services (NAS) 1/12 Outline In order to find communities in a graph one
More informationTowards Modeling Legitimate and Unsolicited Email Traffic Using Social Network Properties
Towards Modeling Legitimate and Unsolicited Email Traffic Using Social Network Properties Farnaz Moradi Tomas Olovsson Philippas Tsigas Computer Science and Engineering Chalmers University of Technology,
More informationMeasuring Propagation in Online Social Networks: The Case of YouTube
Measuring Propagation in Online Social Networks: The Case of YouTube Amir Afrasiabi Rad a.afrasiabi@uottawa.ca EECS Morad Benyoucef Benyoucef@Telfer.uOttawa.ca Telfer School of Management University of
More informationPractical Detection of Spammers and Content Promoters in Online Video Sharing Systems
1 Practical Detection of Spammers and Content Promoters in Online Video Sharing Systems Fabrício Benevenuto, Tiago Rodrigues, Adriano Veloso, Jussara Almeida, Marcos Gonçalves and Virgílio Almeida Computer
More informationResearch Article A Comparison of Online Social Networks and Real-Life Social Networks: A Study of Sina Microblogging
Mathematical Problems in Engineering, Article ID 578713, 6 pages http://dx.doi.org/10.1155/2014/578713 Research Article A Comparison of Online Social Networks and Real-Life Social Networks: A Study of
More informationPredict the Popularity of YouTube Videos Using Early View Data
000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050
More informationA SURVEY OF PROPERTIES AND MODELS OF ON- LINE SOCIAL NETWORKS
International Conference on Mathematical and Computational Models PSG College of Technology, Coimbatore Copyright 2009, Narosa Publishing House, New Delhi, India A SURVEY OF PROPERTIES AND MODELS OF ON-
More informationThe Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth
Cognitive Science 29 (2005) 41 78 Copyright 2005 Cognitive Science Society, Inc. All rights reserved. The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth
More informationUniversiteit Leiden Opleiding Informatica
Internal Report 2011 10 August 2011 Universiteit Leiden Opleiding Informatica Identifying Prominent Actors in Social Networks Iris Hupkens BACHELOR THESIS Leiden Institute of Advanced Computer Science
More informationChapter 12 Extraction and Analysis of Facebook Friendship Relations
Chapter 12 Extraction and Analysis of Facebook Friendship Relations Salvatore Catanese, Pasquale De Meo, Emilio Ferrara, Giacomo Fiumara, and Alessandro Provetti Abstract Online social networks (OSNs)
More informationLocalized Bridging Centrality for Distributed Network Analysis
Localized Bridging Centrality for Distributed Network Analysis Soumendra Nanda and David Kotz Department of Computer Science and Institute for Security Technology Studies, Dartmouth College, Hanover, NH
More informationEmail Information Flow in Large-Scale Enterprises
Email Information Flow in Large-Scale Enterprises Thomas Karagiannis and Milan Vojnovic Microsoft Research {thomkar,milanv}@microsoft.com May 2008 Technical Report MSR-TR-2008-76 Microsoft Research Microsoft
More informationUnderstanding the evolution dynamics of internet topology
Understanding the evolution dynamics of internet topology Shi Zhou* University College London, Adastral Park Campus, Ross Building, Ipswich, IP5 3RE, United Kingdom Received 2 December 2005; revised manuscript
More informationV. Adamchik 1. Graph Theory. Victor Adamchik. Fall of 2005
V. Adamchik 1 Graph Theory Victor Adamchik Fall of 2005 Plan 1. Basic Vocabulary 2. Regular graph 3. Connectivity 4. Representing Graphs Introduction A.Aho and J.Ulman acknowledge that Fundamentally, computer
More information1 Six Degrees of Separation
Networks: Spring 2007 The Small-World Phenomenon David Easley and Jon Kleinberg April 23, 2007 1 Six Degrees of Separation The small-world phenomenon the principle that we are all linked by short chains
More informationDesign of a Crawler for Online Social Networks Analysis
Design of a Crawler for Online Social Networks Analysis Chi-In Wong 1, Kin-Yeung Wong 2, Kuong-Wai Ng 3, Wei Fan 4, Kai-Hau Yeung 5 Computing Program, Macao Polytechnic Institute, R. de Luis Gonzaga Gomes,
More informationDecoding the Structure of the WWW: A Comparative Analysis of Web Crawls
Decoding the Structure of the WWW: A Comparative Analysis of Web Crawls M. ÁNGELES SERRANO Indiana University and Institute for Scientific Interchange, Turin, Italy ANA MAGUITMAN Universidad Nacional del
More informationCYCLON: Inexpensive Membership Management for Unstructured P2P Overlays
CYCLON: Inexpensive Membership Management for Unstructured P2P Overlays RUNNING HEAD: [the same as the title] Spyros Voulgaris 1, Daniela Gavidia 2, Maarten van Steen 3 Contact author: Spyros Voulgaris
More informationPart 2: Community Detection
Chapter 8: Graph Data Part 2: Community Detection Based on Leskovec, Rajaraman, Ullman 2014: Mining of Massive Datasets Big Data Management and Analytics Outline Community Detection - Social networks -
More informationSecond Life: a Social Network of Humans and Bots
Second ife: a Social Network of Humans and Bots Matteo Varvello Alcatel-ucent, Holmdel, USA matteo.varvello@alcatel-lucent.com Geoffrey M. Voelker University of alifornia, San Diego, USA voelker@cs.ucsd.edu
More informationTemporal Dynamics of Scale-Free Networks
Temporal Dynamics of Scale-Free Networks Erez Shmueli, Yaniv Altshuler, and Alex Sandy Pentland MIT Media Lab {shmueli,yanival,sandy}@media.mit.edu Abstract. Many social, biological, and technological
More informationEight Friends Are Enough: Social Graph Approximation via Public Listings
Eight Friends Are Enough: Social Graph Approximation via Public Listings ABSTRACT Joseph Bonneau jcb82@cl.cam.ac.u Fran Stajano fms27@cl.cam.ac.u The popular social networing website Faceboo exposes a
More informationWherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography
Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography Lars Backstrom Dept. of Computer Science Cornell University, Ithaca NY lars@cs.cornell.edu Cynthia Dwork
More informationModule 5: Multiple Regression Analysis
Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College
More informationCMPSCI611: Approximating MAX-CUT Lecture 20
CMPSCI611: Approximating MAX-CUT Lecture 20 For the next two lectures we ll be seeing examples of approximation algorithms for interesting NP-hard problems. Today we consider MAX-CUT, which we proved to
More informationImportance of IP Alias Resolution in Sampling Internet Topologies
Importance of IP Alias Resolution in Sampling Internet Topologies Mehmet Hadi Gunes Department of Computer Science University of Texas at Dallas Email: mgunes@utdallas.edu Kamil Sarac Department of Computer
More informationA Measurement Study of Peer-to-Peer File Sharing Systems
CSF641 P2P Computing 點 對 點 計 算 A Measurement Study of Peer-to-Peer File Sharing Systems Stefan Saroiu, P. Krishna Gummadi, and Steven D. Gribble Department of Computer Science and Engineering University
More informationStructural and functional analytics for community detection in large-scale complex networks
Chopade and Zhan Journal of Big Data DOI 10.1186/s40537-015-0019-y RESEARCH Open Access Structural and functional analytics for community detection in large-scale complex networks Pravin Chopade 1* and
More informationGroup Formation in Large Social Networks: Membership, Growth, and Evolution
Group Formation in Large Social Networks: Membership, Growth, and Evolution Lars Backstrom Dept. of Computer Science Cornell University, Ithaca NY lars@cs.cornell.edu Dan Huttenlocher Dept. of Computer
More informationNetwork/Graph Theory. What is a Network? What is network theory? Graph-based representations. Friendship Network. What makes a problem graph-like?
What is a Network? Network/Graph Theory Network = graph Informally a graph is a set of nodes joined by a set of lines or arrows. 1 1 2 3 2 3 4 5 6 4 5 6 Graph-based representations Representing a problem
More informationGraph Theory and Networks in Biology
Graph Theory and Networks in Biology Oliver Mason and Mark Verwoerd March 14, 2006 Abstract In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss
More informationCS 224W Project Milestone Analysis of the YouTube Channel Recommendation Network
CS 224W Project Milestone Analysis of the YouTube Channel Recommendation Network Ian Torres [itorres] Jacob Conrad Trinidad [j3nidad] December 8th, 2015 I. Introduction With over a billion users, YouTube
More informationReciprocal versus Parasocial Relationships in Online Social Networks
Noname manuscript No. (will be inserted by the editor) Reciprocal versus Parasocial Relationships in Online Social Networks Neil Zhenqiang Gong Wenchang Xu Received: date / Accepted: date Abstract Many
More informationExpression. Variable Equation Polynomial Monomial Add. Area. Volume Surface Space Length Width. Probability. Chance Random Likely Possibility Odds
Isosceles Triangle Congruent Leg Side Expression Equation Polynomial Monomial Radical Square Root Check Times Itself Function Relation One Domain Range Area Volume Surface Space Length Width Quantitative
More informationAttacking 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 informationA Graph-Based Friend Recommendation System Using Genetic Algorithm
WCCI 2010 IEEE World Congress on Computational Intelligence July, 18-23, 2010 - CCIB, Barcelona, Spain CEC IEEE A Graph-Based Friend Recommendation System Using Genetic Algorithm Nitai B. Silva, Ing-Ren
More informationIntroduction to Networks and Business Intelligence
Introduction to Networks and Business Intelligence Prof. Dr. Daning Hu Department of Informatics University of Zurich Sep 17th, 2015 Outline Network Science A Random History Network Analysis Network Topological
More information