# A SOCIAL NETWORK ANALYSIS APPROACH TO ANALYZE ROAD NETWORKS INTRODUCTION

Save this PDF as:

Size: px
Start display at page:

## Transcription

3 Betweenness Betweenness explains how a node can control the other nodes which have no direct connectivity between them. It counts how many times a node intervene to connect the other nodes. It is also one of global measurement of centrality, because betweenness investigate the whole network to find the connecting path. g jk ( Ni ) CBetweennes s ( Ni ) = g j< k jk where, gjk is the number of geodesic paths between two nodes Nj and Nk, and gjk(ni) is the number of geodesic between the Nj and Nk that contain node Ni. Likewise closeness, betweenness is also global centrality, so normalized betweenness is used for the experiments. In a road network application, betweenness tells how the intersection points are important to reach the destination from the start points. Entropy Entropy is a quantitative measurement used to explain the probability distributions. That is an important concept in the information coding theory to be used for measurement the probability distribution. If the probability has uniform distribution, it is called the uncertainty of the distribution is uniform, such that, the states of system are highly disordered. In such cases the entropy of the probability distribution increases. On the contrary, when the probability distribution is not uniform distribution, so some of state could be predictable. It means the probability of predictable states has higher probability than the others. In this case, the state of order is high, and the entropy goes down to the lower. Therefore entropy is helpful tool to describe the state of order in a system. Let P be the probability distribution on the node set of V(G), and p i [0,1]. The entropy of the graph G is N H( G, P) = p i log2( pi ) i= 1 In the previous phase, we computed three types of centralities, and we earned the histogram of those centralities. The histogram is directly converted into the probability distribution, and entropy of the each centrality was computed. EXPERIMENT For the road network structure comparison, we selected four sites from two types of settings. One study area is Columbus Ohio and the other study area is Washington D.C area. From those areas, we selected two sites, one is in a downtown area and the other one is in a residential site. The graphs for the selected sites are generated from the Google Map TM. Figure 1 shows the network topology of selected sites. As can be observed the downtown area has a nearly grid structure, while the residential area is more circular. Figure 1. (a) Network of residential area of Columbus Ohio, (b) Network of downtown area of Columbus Ohio.

4 Figure 2. (a) Network of residential area of Washington D.C. (b) Network of downtown area of Washington D.C. Table 1 shows constructed network properties, and it tells the number of nodes and edges in a network is different even selected site has same coverage of area. The ratio of vertices to edges in residential area is larger than that of in downtown area. Because, the downtown has nearly gird, so many of the nodal degree is four. Table 1. Graph properties of selected study sites Number of Nodes Number of Links Ratio of nodes to Links Columbus Downtown Area Columbus Residential Area Washington D.C Downtown Area Washington D.C Resident Area Centrality of the Networks We found that the degree range in the above network is one to five. The degree of the nodes could not be expanded like as other types of complex network. Because that road network is embedded into 2D plane, it has planar network property. The histogram of degree in figure 3 shows that the downtown area tends to have more nodal degree of four than the residential area. However in a view of probability distribution, degree of one and degree of three in Columbus residential area is similar. And in case of Washington D.C. downtown area, the probability distribution is close in case of degree. Columbus Washington D.C Number of Nodes Residential area Downtown Number of Nodes Resident Area Downtown Degree Degree Figure 3. Histogram of Degree. From the above distribution, degree entropy is computed and shown in figure 4. This figure describes that the entropy of degree distribution dose not discriminate different types of road network topologies. This is cased by the range of the degree is too narrow. So we experiment the entropy with the closeness centrality distribution and betweenness centrality distribution. Figure 5 and figure 6 show the examination results.

5 Figure 5 shows how the closeness is distributed. The closeness in a road network indicates how far from one node to the other nodes. So the larger value of closeness means that a node dose not have a short cut path to arrive to the other nodes. The study sample in Columbus area explains well that the downtown area has more alternative shortest routes to than the residential area. That implies grid-like network topology generate circulated routes, and it has shorter geodesic paths than residential area. Figure 6 shows the probability of distribution of betweenness. Betweenness indicates the controlling power between other nodes which are not connected. Thus the higher value of betweenness means that the node plays an important role to connect the other two nodes. On the contrary, a node having smaller value of betweenness implies it is less important to connect other two nodes. So we assume that those nodes could be a leaf node a network. Graph of figure 6 of Columbus downtown area shows that downtown area has grid type of road networks, so there are more alternative ways to reach to the other node than residential area. Figure 4. Probability distibution of degree in study area and its entropy. CONCLUSION We propose road network analysis applying social network analysis methods which are widely used in the other areas. Figure 6. Probability distribution of closeness and its entropy. First we compute the three types of centralities, degree centrality, closeness centrality, and betweenness centralities. Those node centralities describe how a node important in a network, and we extract the histogram of those centrality values respectively. By comparing the distribution of node centrality in downtown area and residential area, this work showed that the degree distribution dose not have distinction between downtown and residential area. Because road network is planar network so the range of nodal degree is narrow, so the entropy of the degree distribution dose not have difference between downtown area and residential area. However the distribution of closeness and betweenness show difference of downtown area and residential area. In a Figure 5. Probability distribution of betweenness and its entropy. downtown area which having grid-like network topology has higher entropy than the residential area having radiant network topology. In this work we examine the distribution properties in unweighted and undirected network structure. We conjecture that the proposed

6 analysis tool can be extended to include other types of information related to transportation such as traffic flows, accidents, and number of cars on a certain road. We will represent this information as weighted graph, and provide detail report for future road intersection planning. REFERENCE Cardillo, A., S. Scellato, V. Latora, and S. Porta, Structural properties of planar graphs of urban street patterns. Physical Review E Dehmer, M., Information processing in complex networks: Graph entropy and information functionals. Applied Mathematics and Computation 201, 1-2, pp Freeman, L Centrality in social networks: Conceptual clarification. Social Networks 1, 3 pp Ji, L., W. Bing-hong, W. Wen-xu, and Z. Tao, Network entropy based on topology configuration and its computation to random networks. Chinese Physics Letters 25, 11, Knoke, D., and S. Yang, Social Network Analysis (Quantitative Applications in the Social Sciences), 2nd ed. Sage Publications, Inc, November. Masucci, P., D. Smith, A. Crooks, and M. Batty, Random planar graphs and the London street network. Riis, S., Graph entropy, network coding and guessing games. Shetty, J., and J. Adibi, J., Discovering important nodes through graph entropy the case of enron database. In LinkKDD 05: Proceedings of the 3rd international workshop on Link discovery (New York, NY, USA, 2005), ACM, pp Simonyi, G., Graph entropy: a survey. In Combinatorial Optimization, DIMACS Series in Discrete Math. and Theoretical Comp. Sci., Vol. 20 (W. Cook, L. Lovász and P. D. Seymour, eds.), pp Wasserman, S., K. Faust, and D. Iacobucci, Social Network Analysis : Methods and Applications(Structural Analysis in the Social Sciences). Cambridge University Press, November. Wu, D., and X. Hu, Mining and analyzing the topological structure of protein-protein interaction networks. In SAC 06: Proceedings of the 2006 ACM symposium on Applied computing (New York, NY, USA, 2006), ACM, pp

### 1.204 Final Project Network Analysis of Urban Street Patterns

1.204 Final Project Network Analysis of Urban Street Patterns Michael T. Chang December 21, 2012 1 Introduction In this project, I analyze road networks as a planar graph and use tools from network analysis

### Network (Tree) Topology Inference Based on Prüfer Sequence

Network (Tree) Topology Inference Based on Prüfer Sequence C. Vanniarajan and Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai 600036 vanniarajanc@hcl.in,

### Week 3. Network Data; Introduction to Graph Theory and Sociometric Notation

Wasserman, Stanley, and Katherine Faust. 2009. Social Network Analysis: Methods and Applications, Structural Analysis in the Social Sciences. New York, NY: Cambridge University Press. Chapter III: Notation

### 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

### Managing Trust in Self-organized Mobile Ad Hoc Networks

Managing Trust in Self-organized Mobile Ad Hoc Networks John S. Baras and Tao Jiang Electrical and Computer Engineering Department Computer Science Department and the Institute for Systems Research University

### Offline 1-Minesweeper is NP-complete

Offline 1-Minesweeper is NP-complete James D. Fix Brandon McPhail May 24 Abstract We use Minesweeper to illustrate NP-completeness proofs, arguments that establish the hardness of solving certain problems.

### 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

### USE OF EIGENVALUES AND EIGENVECTORS TO ANALYZE BIPARTIVITY OF NETWORK GRAPHS

USE OF EIGENVALUES AND EIGENVECTORS TO ANALYZE BIPARTIVITY OF NETWORK GRAPHS Natarajan Meghanathan Jackson State University, 1400 Lynch St, Jackson, MS, USA natarajan.meghanathan@jsums.edu ABSTRACT This

### Social Network Discovery based on Sensitivity Analysis

Social Network Discovery based on Sensitivity Analysis Tarik Crnovrsanin, Carlos D. Correa and Kwan-Liu Ma Department of Computer Science University of California, Davis tecrnovrsanin@ucdavis.edu, {correac,ma}@cs.ucdavis.edu

### Practical 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

### Graph 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

### Data Structures in Java. Session 16 Instructor: Bert Huang

Data Structures in Java Session 16 Instructor: Bert Huang http://www.cs.columbia.edu/~bert/courses/3134 Announcements Homework 4 due next class Remaining grades: hw4, hw5, hw6 25% Final exam 30% Midterm

### 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

### Continuous Fastest Path Planning in Road Networks by Mining Real-Time Traffic Event Information

Continuous Fastest Path Planning in Road Networks by Mining Real-Time Traffic Event Information Eric Hsueh-Chan Lu Chi-Wei Huang Vincent S. Tseng Institute of Computer Science and Information Engineering

### 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

### Simulations in Statistical Physics

Page 1 Simulations in Statistical Physics Course for MSc physics students Janos Török Department of Theoretical Physics November 25, 2014 Page 2 Algorithmically defined models Self-Organized Criticality

### Approximated Distributed Minimum Vertex Cover Algorithms for Bounded Degree Graphs

Approximated Distributed Minimum Vertex Cover Algorithms for Bounded Degree Graphs Yong Zhang 1.2, Francis Y.L. Chin 2, and Hing-Fung Ting 2 1 College of Mathematics and Computer Science, Hebei University,

### Existence of Simple Tours of Imprecise Points

Existence of Simple Tours of Imprecise Points Maarten Löffler Department of Information and Computing Sciences, Utrecht University Technical Report UU-CS-007-00 www.cs.uu.nl ISSN: 09-7 Existence of Simple

### Structural properties of urban street networks of varying population density

Structural properties of urban street networks of varying Dimitris Maniadakis and Dimitris Varoutas Department of Informatics and Telecommunications University of Athens Athens, Greece {D.Maniadakis, D.Varoutas}@di.uoa.gr

### Randomized flow model and centrality measure for electrical power transmission network analysis

Randomized flow model and centrality measure for electrical power transmission network analysis Enrico Zio, Roberta Piccinelli To cite this version: Enrico Zio, Roberta Piccinelli. Randomized flow model

### Effects of node buffer and capacity on network traffic

Chin. Phys. B Vol. 21, No. 9 (212) 9892 Effects of node buffer and capacity on network traffic Ling Xiang( 凌 翔 ) a), Hu Mao-Bin( 胡 茂 彬 ) b), and Ding Jian-Xun( 丁 建 勋 ) a) a) School of Transportation Engineering,

### Complex Network Visualization based on Voronoi Diagram and Smoothed-particle Hydrodynamics

Complex Network Visualization based on Voronoi Diagram and Smoothed-particle Hydrodynamics Zhao Wenbin 1, Zhao Zhengxu 2 1 School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu

### Urban Road Network and Taxi Network Modeling Based on Complex Network Theory

Journal of Information Hiding and Multimedia Signal Processing c 2016 ISSN 2073-4212 Ubiquitous International Volume 7, Number 3, May 2016 Urban Road Network and Taxi Network Modeling Based on Complex

### A Virtual Machine Searching Method in Networks using a Vector Space Model and Routing Table Tree Architecture

A Virtual Machine Searching Method in Networks using a Vector Space Model and Routing Table Tree Architecture Hyeon seok O, Namgi Kim1, Byoung-Dai Lee dept. of Computer Science. Kyonggi University, Suwon,

### CONCEPTUAL MODEL OF MULTI-AGENT BUSINESS COLLABORATION BASED ON CLOUD WORKFLOW

CONCEPTUAL MODEL OF MULTI-AGENT BUSINESS COLLABORATION BASED ON CLOUD WORKFLOW 1 XINQIN GAO, 2 MINGSHUN YANG, 3 YONG LIU, 4 XIAOLI HOU School of Mechanical and Precision Instrument Engineering, Xi'an University

### RANKING AND VISUALIZING THE KEYWORDS IN MISSION STATEMENTS OF SLOVENIAN COMPANIES

RANKING AND VISUALIZING THE KEYWORDS IN MISSION STATEMENTS OF SLOVENIAN COMPANIES Kristijan Breznik International School for Social and Business Studies, Slovenia kristijan.breznik@mfdps.si Abstract: Mission

### A comparative study of social network analysis tools

Membre de Membre de A comparative study of social network analysis tools David Combe, Christine Largeron, Előd Egyed-Zsigmond and Mathias Géry International Workshop on Web Intelligence and Virtual Enterprises

### Consecutive Geographic Multicasting Protocol in Large-Scale Wireless Sensor Networks

21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications Consecutive Geographic Multicasting Protocol in Large-Scale Wireless Sensor Networks Jeongcheol Lee, Euisin

### Application of Adaptive Probing for Fault Diagnosis in Computer Networks 1

Application of Adaptive Probing for Fault Diagnosis in Computer Networks 1 Maitreya Natu Dept. of Computer and Information Sciences University of Delaware, Newark, DE, USA, 19716 Email: natu@cis.udel.edu

### Graph Mining Techniques for Social Media Analysis

Graph Mining Techniques for Social Media Analysis Mary McGlohon Christos Faloutsos 1 1-1 What is graph mining? Extracting useful knowledge (patterns, outliers, etc.) from structured data that can be represented

### Agent Mobility Model based on Street Centrality

Agent Mobility Model based on Street Centrality Rupert Leigh Department of Geomatics University of Melbourne ABSTRACT Current agent mobility models are based on random movement. This means that the probability

### Cluster detection algorithm in neural networks

Cluster detection algorithm in neural networks David Meunier and Hélène Paugam-Moisy Institute for Cognitive Science, UMR CNRS 5015 67, boulevard Pinel F-69675 BRON - France E-mail: {dmeunier,hpaugam}@isc.cnrs.fr

### FCE: A Fast Content Expression for Server-based Computing

FCE: A Fast Content Expression for Server-based Computing Qiao Li Mentor Graphics Corporation 11 Ridder Park Drive San Jose, CA 95131, U.S.A. Email: qiao li@mentor.com Fei Li Department of Computer Science

### SGL: Stata graph library for network analysis

SGL: Stata graph library for network analysis Hirotaka Miura Federal Reserve Bank of San Francisco Stata Conference Chicago 2011 The views presented here are my own and do not necessarily represent the

### Efficient Integration of Data Mining Techniques in Database Management Systems

Efficient Integration of Data Mining Techniques in Database Management Systems Fadila Bentayeb Jérôme Darmont Cédric Udréa ERIC, University of Lyon 2 5 avenue Pierre Mendès-France 69676 Bron Cedex France

### How to Analyze Company Using Social Network?

How to Analyze Company Using Social Network? Sebastian Palus 1, Piotr Bródka 1, Przemysław Kazienko 1 1 Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland {sebastian.palus,

### Technology, Kolkata, INDIA, pal.sanjaykumar@gmail.com. sssarma2001@yahoo.com

Sanjay Kumar Pal 1 and Samar Sen Sarma 2 1 Department of Computer Science & Applications, NSHM College of Management & Technology, Kolkata, INDIA, pal.sanjaykumar@gmail.com 2 Department of Computer Science

### Distance Degree Sequences for Network Analysis

Universität Konstanz Computer & Information Science Algorithmics Group 15 Mar 2005 based on Palmer, Gibbons, and Faloutsos: ANF A Fast and Scalable Tool for Data Mining in Massive Graphs, SIGKDD 02. Motivation

### WEB SITE OPTIMIZATION THROUGH MINING USER NAVIGATIONAL PATTERNS

WEB SITE OPTIMIZATION THROUGH MINING USER NAVIGATIONAL PATTERNS Biswajit Biswal Oracle Corporation biswajit.biswal@oracle.com ABSTRACT With the World Wide Web (www) s ubiquity increase and the rapid development

### Graph Algorithms. Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar

Graph Algorithms Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar To accompany the text Introduction to Parallel Computing, Addison Wesley, 3. Topic Overview Definitions and Representation Minimum

### Labeling outerplanar graphs with maximum degree three

Labeling outerplanar graphs with maximum degree three Xiangwen Li 1 and Sanming Zhou 2 1 Department of Mathematics Huazhong Normal University, Wuhan 430079, China 2 Department of Mathematics and Statistics

### The mathematics of networks

The mathematics of networks M. E. J. Newman Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109 1040 In much of economic theory it is assumed that economic agents interact,

### Influence Discovery in Semantic Networks: An Initial Approach

2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation Influence Discovery in Semantic Networks: An Initial Approach Marcello Trovati and Ovidiu Bagdasar School of Computing

### Part 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 -

### Traffic Prediction in Wireless Mesh Networks Using Process Mining Algorithms

Traffic Prediction in Wireless Mesh Networks Using Process Mining Algorithms Kirill Krinkin Open Source and Linux lab Saint Petersburg, Russia kirill.krinkin@fruct.org Eugene Kalishenko Saint Petersburg

### Research experiences for undergraduate faculty

Research experiences for undergraduate faculty organized by Leslie Hogben and Roselyn Williams Workshop Summary On July 20-24, 2009, the American Institute of Mathematics (AIM), with support from the National

### Diameter and Treewidth in Minor-Closed Graph Families, Revisited

Algorithmica manuscript No. (will be inserted by the editor) Diameter and Treewidth in Minor-Closed Graph Families, Revisited Erik D. Demaine, MohammadTaghi Hajiaghayi MIT Computer Science and Artificial

### Image Compression through DCT and Huffman Coding Technique

International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2015 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Rahul

### EFFICIENT DETECTION IN DDOS ATTACK FOR TOPOLOGY GRAPH DEPENDENT PERFORMANCE IN PPM LARGE SCALE IPTRACEBACK

EFFICIENT DETECTION IN DDOS ATTACK FOR TOPOLOGY GRAPH DEPENDENT PERFORMANCE IN PPM LARGE SCALE IPTRACEBACK S.Abarna 1, R.Padmapriya 2 1 Mphil Scholar, 2 Assistant Professor, Department of Computer Science,

### Competitive Analysis of On line Randomized Call Control in Cellular Networks

Competitive Analysis of On line Randomized Call Control in Cellular Networks Ioannis Caragiannis Christos Kaklamanis Evi Papaioannou Abstract In this paper we address an important communication issue arising

### Application of Graph Theory to

Application of Graph Theory to Requirements Traceability A methodology for visualization of large requirements sets Sam Brown L-3 Communications This presentation consists of L-3 STRATIS general capabilities

### A 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

### An Overview of Knowledge Discovery Database and Data mining Techniques

An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,

### Visualising Space Time Dynamics: Plots and Clocks, Graphs and Maps

The International Symposium on Spatial Temporal Data Mining & Geo Computation at UCL, July 19 23, 2011 Visualising Space Time Dynamics: Plots and Clocks, Graphs and Maps Michael Batty Martin Austwick Ollie

### A note on properties for a complementary graph and its tree graph

A note on properties for a complementary graph and its tree graph Abulimiti Yiming Department of Mathematics Institute of Mathematics & Informations Xinjiang Normal University China Masami Yasuda Department

### Data Exploration with GIS Viewsheds and Social Network Analysis

Data Exploration with GIS Viewsheds and Social Network Analysis Giles Oatley 1, Tom Crick 1 and Ray Howell 2 1 Department of Computing, Cardiff Metropolitan University, UK 2 Faculty of Business and Society,

### Author(s)Kiyomi, Masashi; Saitoh, Toshiki; Ue.

JAIST Reposi https://dspace.j Title Voronoi Game on a Path Author(s)Kiyomi, Masashi; Saitoh, Toshiki; Ue Citation IEICE TRANSACTIONS on Information an E94-D(6): 1185-1189 Issue Date 2011-06-01 Type Journal

### CHAPTER 1 INTRODUCTION

1 CHAPTER 1 INTRODUCTION Exploration is a process of discovery. In the database exploration process, an analyst executes a sequence of transformations over a collection of data structures to discover useful

### An Active Packet can be classified as

Mobile Agents for Active Network Management By Rumeel Kazi and Patricia Morreale Stevens Institute of Technology Contact: rkazi,pat@ati.stevens-tech.edu Abstract-Traditionally, network management systems

### Taxicab Geometry, or When a Circle is a Square. Circle on the Road 2012, Math Festival

Taxicab Geometry, or When a Circle is a Square Circle on the Road 2012, Math Festival Olga Radko (Los Angeles Math Circle, UCLA) April 14th, 2012 Abstract The distance between two points is the length

### An Initial Survey of Fractional Graph and Table Area in Behavioral Journals

The Behavior Analyst 2008, 31, 61 66 No. 1 (Spring) An Initial Survey of Fractional Graph and Table Area in Behavioral Journals Richard M. Kubina, Jr., Douglas E. Kostewicz, and Shawn M. Datchuk The Pennsylvania

### 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!)

### Probabilistic Graphical Models

Probabilistic Graphical Models Raquel Urtasun and Tamir Hazan TTI Chicago April 4, 2011 Raquel Urtasun and Tamir Hazan (TTI-C) Graphical Models April 4, 2011 1 / 22 Bayesian Networks and independences

### HISTORICAL DEVELOPMENTS AND THEORETICAL APPROACHES IN SOCIOLOGY Vol. I - Social Network Analysis - Wouter de Nooy

SOCIAL NETWORK ANALYSIS University of Amsterdam, Netherlands Keywords: Social networks, structuralism, cohesion, brokerage, stratification, network analysis, methods, graph theory, statistical models Contents

### Graph. Consider a graph, G in Fig Then the vertex V and edge E can be represented as:

Graph A graph G consist of 1. Set of vertices V (called nodes), (V = {v1, v2, v3, v4...}) and 2. Set of edges E (i.e., E {e1, e2, e3...cm} A graph can be represents as G = (V, E), where V is a finite and

### Scalar Visualization

Scalar Visualization 4-1 Motivation Visualizing scalar data is frequently encountered in science, engineering, and medicine, but also in daily life. Recalling from earlier, scalar datasets, or scalar fields,

### QUALITY OF SERVICE METRICS FOR DATA TRANSMISSION IN MESH TOPOLOGIES

QUALITY OF SERVICE METRICS FOR DATA TRANSMISSION IN MESH TOPOLOGIES SWATHI NANDURI * ZAHOOR-UL-HUQ * Master of Technology, Associate Professor, G. Pulla Reddy Engineering College, G. Pulla Reddy Engineering

### Graph Algorithms using Map-Reduce

Graph Algorithms using Map-Reduce Graphs are ubiquitous in modern society. Some examples: The hyperlink structure of the web 1/7 Graph Algorithms using Map-Reduce Graphs are ubiquitous in modern society.

### Applying Social Network Analysis to the Information in CVS Repositories

Applying Social Network Analysis to the Information in CVS Repositories Luis Lopez-Fernandez, Gregorio Robles, Jesus M. Gonzalez-Barahona GSyC, Universidad Rey Juan Carlos {llopez,grex,jgb}@gsyc.escet.urjc.es

### Analysis of an Artificial Hormone System (Extended abstract)

c 2013. This is the author s version of the work. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purpose or for creating

### A Direct Numerical Method for Observability Analysis

IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 15, NO 2, MAY 2000 625 A Direct Numerical Method for Observability Analysis Bei Gou and Ali Abur, Senior Member, IEEE Abstract This paper presents an algebraic method

### An Order-Invariant Time Series Distance Measure [Position on Recent Developments in Time Series Analysis]

An Order-Invariant Time Series Distance Measure [Position on Recent Developments in Time Series Analysis] Stephan Spiegel and Sahin Albayrak DAI-Lab, Technische Universität Berlin, Ernst-Reuter-Platz 7,

### On Reliability of Dynamic Addressing Routing Protocols in Mobile Ad Hoc Networks

On Reliability of Dynamic Addressing Routing Protocols in Mobile Ad Hoc Networks Marcello Caleffi, Giancarlo Ferraiuolo, Luigi Paura Department of Electronic and Telecommunication Engineering (DIET) University

### Multi-layer Structure of Data Center Based on Steiner Triple System

Journal of Computational Information Systems 9: 11 (2013) 4371 4378 Available at http://www.jofcis.com Multi-layer Structure of Data Center Based on Steiner Triple System Jianfei ZHANG 1, Zhiyi FANG 1,

### Equivalence Concepts for Social Networks

Equivalence Concepts for Social Networks Tom A.B. Snijders University of Oxford March 26, 2009 c Tom A.B. Snijders (University of Oxford) Equivalences in networks March 26, 2009 1 / 40 Outline Structural

### Graph Theory for Articulated Bodies

Graph Theory for Articulated Bodies Alba Perez-Gracia Department of Mechanical Engineering, Idaho State University Articulated Bodies A set of rigid bodies (links) joined by joints that allow relative

### Load balancing in a heterogeneous computer system by self-organizing Kohonen network

Bull. Nov. Comp. Center, Comp. Science, 25 (2006), 69 74 c 2006 NCC Publisher Load balancing in a heterogeneous computer system by self-organizing Kohonen network Mikhail S. Tarkov, Yakov S. Bezrukov Abstract.

### Intuitive and Effective Design of Periodic Symmetric Tiles

Intuitive and Effective Design of Periodic Symmetric Tiles Ergun Akleman, Texas A&M University, USA Jianer Chen, Texas A&M University, USA Burak Meric, Knowledge Based Information Systems, Inc., USA Abstract

### Network Formation: Bilateral Contracting and Myopic Dynamics

and and Esteban 1 Ramesh 2 Shie 3 1 Institute for Computational and Mathematical Engineering Stanford University 2 Department of Management Science and Engineering Stanford University 3 Department of Electrical

### Current Standard: Mathematical Concepts and Applications Shape, Space, and Measurement- Primary

Shape, Space, and Measurement- Primary A student shall apply concepts of shape, space, and measurement to solve problems involving two- and three-dimensional shapes by demonstrating an understanding of:

### Detection of local affinity patterns in big data

Detection of local affinity patterns in big data Andrea Marinoni, Paolo Gamba Department of Electronics, University of Pavia, Italy Abstract Mining information in Big Data requires to design a new class

### most 4 Mirka Miller 1,2, Guillermo Pineda-Villavicencio 3, The University of Newcastle Callaghan, NSW 2308, Australia University of West Bohemia

Complete catalogue of graphs of maimum degree 3 and defect at most 4 Mirka Miller 1,2, Guillermo Pineda-Villavicencio 3, 1 School of Electrical Engineering and Computer Science The University of Newcastle

### A MEASURE OF GLOBAL EFFICIENCY IN NETWORKS. Aysun Aytac 1, Betul Atay 2. Faculty of Science Ege University 35100, Bornova, Izmir, TURKEY

International Journal of Pure and Applied Mathematics Volume 03 No. 05, 6-70 ISSN: 3-8080 (printed version); ISSN: 34-3395 (on-line version) url: http://www.ijpam.eu doi: http://dx.doi.org/0.73/ijpam.v03i.5

### Building Alternate Multicasting Trees in MPLS Networks

Treasures at UT Dallas Eric Jonsson School of Engineering and Computer Science 5-2009 Building Alternate Multicasting Trees in MPLS Networks Limin Tang, et al. Follow this and additional works at: http://

### Two General Methods to Reduce Delay and Change of Enumeration Algorithms

ISSN 1346-5597 NII Technical Report Two General Methods to Reduce Delay and Change of Enumeration Algorithms Takeaki Uno NII-2003-004E Apr.2003 Two General Methods to Reduce Delay and Change of Enumeration

### Clique coloring B 1 -EPG graphs

Clique coloring B 1 -EPG graphs Flavia Bonomo a,c, María Pía Mazzoleni b,c, and Maya Stein d a Departamento de Computación, FCEN-UBA, Buenos Aires, Argentina. b Departamento de Matemática, FCE-UNLP, La

### Graph 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

### Protographs: Graph-Based Approach to NetFlow Analysis. Jeff Janies RedJack FloCon 2011

Protographs: Graph-Based Approach to NetFlow Analysis Jeff Janies RedJack FloCon 2011 Thesis Using social networks we can complement our existing volumetric analysis. Identify phenomenon we are missing

### SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH

31 Kragujevac J. Math. 25 (2003) 31 49. SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH Kinkar Ch. Das Department of Mathematics, Indian Institute of Technology, Kharagpur 721302, W.B.,

### Dr. Antony Selvadoss Thanamani, Head & Associate Professor, Department of Computer Science, NGM College, Pollachi, India.

Enhanced Approach on Web Page Classification Using Machine Learning Technique S.Gowri Shanthi Research Scholar, Department of Computer Science, NGM College, Pollachi, India. Dr. Antony Selvadoss Thanamani,

### COMMON CORE STATE STANDARDS FOR

COMMON CORE STATE STANDARDS FOR Mathematics (CCSSM) High School Statistics and Probability Mathematics High School Statistics and Probability Decisions or predictions are often based on data numbers in

### Collapse by Cascading Failures in Hybrid Attacked Regional Internet

Collapse by Cascading Failures in Hybrid Attacked Regional Internet Ye Xu and Zhuo Wang College of Information Science and Engineering, Shenyang Ligong University, Shenyang China xuy.mail@gmail.com Abstract

### Analysis of Algorithms, I

Analysis of Algorithms, I CSOR W4231.002 Eleni Drinea Computer Science Department Columbia University Thursday, February 26, 2015 Outline 1 Recap 2 Representing graphs 3 Breadth-first search (BFS) 4 Applications

### Schedule Risk Analysis Simulator using Beta Distribution

Schedule Risk Analysis Simulator using Beta Distribution Isha Sharma Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, Haryana (INDIA) ishasharma211@yahoo.com Dr. P.K.

### SECTIONS 1.5-1.6 NOTES ON GRAPH THEORY NOTATION AND ITS USE IN THE STUDY OF SPARSE SYMMETRIC MATRICES

SECIONS.5-.6 NOES ON GRPH HEORY NOION ND IS USE IN HE SUDY OF SPRSE SYMMERIC MRICES graph G ( X, E) consists of a finite set of nodes or vertices X and edges E. EXMPLE : road map of part of British Columbia

### princeton univ. F 13 cos 521: Advanced Algorithm Design Lecture 6: Provable Approximation via Linear Programming Lecturer: Sanjeev Arora

princeton univ. F 13 cos 521: Advanced Algorithm Design Lecture 6: Provable Approximation via Linear Programming Lecturer: Sanjeev Arora Scribe: One of the running themes in this course is the notion of

### HSI BASED COLOUR IMAGE EQUALIZATION USING ITERATIVE n th ROOT AND n th POWER

HSI BASED COLOUR IMAGE EQUALIZATION USING ITERATIVE n th ROOT AND n th POWER Gholamreza Anbarjafari icv Group, IMS Lab, Institute of Technology, University of Tartu, Tartu 50411, Estonia sjafari@ut.ee

### Midterm Practice Problems

6.042/8.062J Mathematics for Computer Science October 2, 200 Tom Leighton, Marten van Dijk, and Brooke Cowan Midterm Practice Problems Problem. [0 points] In problem set you showed that the nand operator