Visualization of Bibliographic Networks with a Reshaped Landscape

We describe a novel approach to visualize bibliographic networks that facilitates the simultaneous identification of clusters (e.g., topic areas) and prominent entities (e.g., surveys or landmark papers). While employing the landscape metaphor proposed in several earlier works, we introduce new means to determine relevant parameters of the landscape. Moreover, we are able to compute prominent entities, clustering of entities, and the landscape’s surface in a surprisingly simple and uniform way. The effectiveness of our network visualizations is illustrated on

data from the graph drawing literature. Categories and Subject Descriptors (according to ACM CCS): H.3.3 [Information Search and Retrieval]: Informa- tion filtering 1. Introduction Bibliographic analysis24 uses publication data to structure and summarize a scientific field. These data are often given in the form of networks, with nodes representing authors, journals, or publications, and edges representing relations between these entities such as authorship, collaboration, or citation. We present an approach to analyze and visualize biblio- graphic networks using uniform algorithms to determine the prominent entities in the network, to spatially represent the clustering of the network, and to compute a surface for a landscape visualization of results. Since we propose an integrated method of analysis and visualization directed at particular aspects of bibliographic analysis, it may serve as a specialized component in more elaborate systems,10; 5; 9 and in particular as a communica- tion/exploration back-end for systems that specialize in ex- tracting and presenting network data.7; 23 This paper is organized as follows. In Sect. 2 we recall the definition of Kleinberg’s hubs & authorities indices15 and sketch their use in the analysis of bibliographic data. Based on similar principles, a new method for two-dimensional layout of bibliographic networks preserving the scientific topography is presented in Sect. 3. In Sect. 4, index and layout are turned into a landscape visualization, again us- ing the same algorithmic principles. An illustrative example comprised of publications in proceedings of Graph Drawing Symposia is given in Sect. 5. 2. Landmark Papers To identify prominent entities in bibliographic networks, we determine the structural importance of vertices according to their position in the graph. Many concepts formalizing this notion are in use, but the concept of hubs & authorities,15 though originally conceived to improve relevance ranking in Web search engines, appears to be particularly suitable for bibliographic networks. In this section, we present an alter- native derivation of these indices to emphasize the similarity of their computation with those in later sections. We assume familiarity with basic matrix properties and computations.12 A straightforward notion of prominence in undirected graphs, commonly applied in the analysis of social net- works,22 is the idea that the importance of a vertex is de- termined by the importance of its neighbors. According to the following definition, the importance assigned to a vertex is proportional to the total importance of its neighbors. Definition 1 (eigenvector centrality4) Let A be the adja- cency...

Website: www.informatik.uni-konstanz.de | Filesize: -
No of Page(s): 7
Download Visualization of Bibliographic Networks with a Reshaped Landscape ....pdf

FNV: Light-weight Flash-based network

Motivation: Network diagrams are commonly used to visualize biochemical pathways by displaying the relationships between genes, proteins, mRNAs, microRNAs, metabolites, regulatory DNA elements, diseases, viruses, and drugs. While there are several currently available web-based pathway viewers, there is still room for improvement. To this end, we have developed a Flash-based network viewer (FNV) for the visualization of small to moderately sized biological networks and pathways. Summary: Written in Adobe ActionScript 3.0

the viewer accepts simple Extensible Markup Language (XML) formatted input files to display pathways in vector graphics on any web-page providing flexible layout options, interactivity with the user through tool tips, hyperlinks, and the ability to rearrange nodes on the screen. FNV was utilized as a component in several web-based systems, namely Genes2Networks, Lists2Networks, KEA, ChEA and PathwayGene- rator. In addition, FVN can be used to embed pathways inside PDF files for the communication of pathways in soft publication materials. Availability: FNV is available for use and download along with sup- porting documentation and sample networks at http://www.maayanlab.net/FNV. Contact: avi.maayan@mssm.edu 1 INTRODUCTION Pathway databases such KEGG (Ogata et al., 1999), BioCarta (http://www.biocarta.com), WikiPathways (Pico et al., 2008) Science Signaling Connection Maps (Gough, 2002), and UCSD- Nature Signaling Gateway (Saunders et al., 2008) communicate over the web: cell signaling, transcriptional, and metabolic path- ways, as diagrams made of nodes and links. Such diagrams are visualized using different layout algorithms embedded in network viewers implemented with a variety of technologies. The majority of web-based network viewers make use of the Java web technolo- gies. For example, PATIKAweb (Dogrusoz et al., 2006) uses Java Server Pages (JSP) to retrieve information stored in the manually curated PATIKA database, or passed through a file to generate pathway diagrams using a force-directed algorithm to arrange stat- ic images of nodes and edges. Tools such as WebInterViewer (Han et al. 2004) and VisANT (Hu et al., 2008) are useful for large pro- * To whom correspondence should be addressed. tein-protein interaction networks utilizing JavaWebStart. However, JavaWebStart runs in a sandbox and does not easily communicate with the browser. Other tools such as jSquid, (Klammer et al., 2007) are powerful but since they utilize Java Applets they are slow to start and are inconsistent across browsers. Several web- based pathway viewers have been implemented without the use of Java. For example, CellDesigner (Funahashi et al., 2003) used by KEGG, creates static network images with hyperlinks. It was used, for example, by BioPP (Viswanathan, 2007) with Perl and CGI displaying static images with annotations mapped to the nodes as hyperlinks. AVIS (Berger et al., 2007), a network drawing tool that we developed, uses Asynchronous JavaScript and XML (AJAX) and underlying Perl libraries to draw static networks that are ren- dered using GraphViz. GraphViz (Gansner and North, 1999) is one of the most commonly used graph drawing tool for displaying...

Website: bioinformatics.oxfordjournals.org | Filesize: -
No of Page(s): 2
Download FNV: Light-weight Flash-based network and ... - Bioinformatics.pdf

The Development of Social Network Analysis

Social Network Analysis 26.2 Visualization Principles Illustrative Example Substance, Design, Algorithm 26.3 Substance-based Designs Prominence Cohesion Two-mode networks Dynamics 26.4 Trends and Challenges Social networks provide a rich source of graph drawing problems, because they appear in an incredibly wide variety of forms and contexts. After sketching the scope of social network analysis, we establish some general principles for social network visualization before nally reviewing applications of, and challenges for, graph drawing methods in this area. Other accounts more generally relating to the status of visualization in social network analysis are given, e.g., in [Klo81, BKR+99, Fre00, Fre05,

BKR06]. Surveys that are more comprehensive on information visualization approaches, interaction, and network applications from social media are given in [CM11, RF10, CY10]. 26.1 Social Network Analysis The fundamental assumption underlying social network theory is the idea that seemingly autonomous individuals and organizations are in fact embedded in social relations and interactions [BMBL09]. The term social network was coined to delineate the relational perspective from other research traditions on social groups and social categories [Bar54]. In general, a social network consists of actors (e.g., persons, organizations) and some form of (often, but not necessarily: social) relation among them. The network structure is usually modeled as a graph, in which vertices represent actors, and edges represent ties, i.e., the existence of a relation between two actors. Since traits of actors and ties may be important, both vertices and edges can have a multitude of attributes. We will use graph terminology for everything relating to the data model, and social network terminology when referring to substantive aspects. While attributed graph models are indeed at the heart of formal treatments, it is worth noting that theoretically justi ed data models are not as obvious as it may seem [But09]. In fact, social network analysis is maturing into a paradigm of distinct structural theories and associated relational methods. General introductions and methodological overviews can be found in [WB88, WF94, Sco00, CSW05, BE05], a historic account in [Fre04a], and a comprehensive collection of in uential articles in [Fre08]. c 2005 by CRC Press Figure 26.1 A sociogram from [Mor53, p. 422] showing a graph with fourteen highlighted vertices and four clusters. In social network reseach it is important to clarify whether the networks are considered dependent or explanatory variables. In the former case the interest is in why and how networks form the way they do, and in the latter case the interest is in why and how networks in uence other outcomes. For convenience, we will refer to the former as network theory (studying network formation) and to the latter as network analysis (studying network e ects). A major distinction from non-network approaches is that the unit of analysis is the dyad, i.e. a pair of actors (may they be linked or not) rather than a monad (a singleton actor). The methodological toolbox can be organized into the following main compartments. Indexing The assignment of values to predetermined substructures of any size. Most common...

Website: www.informatik.uni-konstanz.de | Filesize: -
No of Page(s): 34
Download The Development of Social Network Analysis—with an.pdf

Finding community structure in very large networks

The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n

vertices and m edges is O(mdlog n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m n and d log n, in which case our algorithm runs in essentially linear time, O(nlog2 n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web-site of a large online retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2 million edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers. I. INTRODUCTION Many systems of current interest to the scienti c com- munity can usefully be represented as networks [1{4]. Examples include the Internet [5] and the world-wide web [6, 7], social networks [8], citation networks [9, 10], food webs [11], and biochemical networks [12, 13]. Each of these networks consists of a set of nodes or vertices representing, for instance, computers or routers on the Internet or people in a social network, connected together by links or edges, representing data connections between computers, friendships between people, and so forth. One network feature that has been emphasized in recent work is community structure, the gathering of vertices into groups such that there is a higher den- sity of edges within groups than between them [14]. The problem of detecting such communities within net- works has been well studied. Early approaches such as the Kernighan{Lin algorithm [15], spectral partition- ing [16, 17], or hierarchical clustering [18] work well for speci c types of problems (particularly graph bisection or problems with well de ned vertex similarity measures), but perform poorly in more general cases [19]. To combat this problem a number of new algorithms have been proposed in recent years. Girvan and New- man [20, 21] proposed a divisive algorithm that uses edge betweenness as a metric to identify the boundaries of communities. This algorithm has been applied suc- cessfully to a variety of networks, including networks of email messages, human and animal social networks, net- works of collaborations between scientists and musicians, metabolic networks and gene networks [20, 22{30]. How- ever, as noted in [21], the algorithm makes heavy de- mands on computational resources, running in O(m2n) time on...

Website: www.ece.unm.edu | Filesize: -
No of Page(s): 6
Download Finding community structure in very large networks.pdf

Analysis and Visualization of Social Networks

We describe visone, a tool that facilitates the visual exploration of social networks. Social network analysis is a methodological approach in the social sciences using graph-theoretic concepts to describe, understand and explain social structure. The visone software is an attempt to integrate analysis and visualization of social networks and is intended to be used in research and teaching. While we are primarily focussing on users in the social sciences, several features provided in the tool will be useful in

other flelds as well. In contrast to more conventional mathematical software in the social sci- ences that aim at providing a comprehensive suite of analytical options, our emphasisison complementing every option we provide with tailored means of graphical interaction. We attempt to make complicated types of analysis and data handling transparent, intuitive, and more readily accessible. User feed- back indicates that many who usually regard data exploration and analysis complicated and unnerving enjoy the playful nature of visual interaction. Consequently, much of the tool is about graph drawing methods speciflcally adapted to facilitate visual data exploration. The origins of visone lie in an interdisciplinary cooperation with researchers from political science which resulted in innovative uses of graph drawing methods for social network visualization, and prototypical implementations thereof. With the growing demand for access to these methods, we started implementing an integrated tool for public use. It should be stressed, however, that visone remains a research platform and testbed for innovative methods, and is not intended to become ? Many people have contributed directly or indirectly to the current state of our tool. We thank Sabine Cornelsen, Patrick Kenis, J˜org Raab, and Volker Schneider for many years of fruitful cooperation, and the participants of POLNET summer schools for their feedback and suggestions. We are indebted to Michael Baur, Marc Benkert, Marco Gaertler, Boris K˜opf, and J˜urgen Lerner for their implementation efiorts, and gratefully acknowledge flnancial support from the Deutsche Forschungsgemeinschaft (DFG) under grant BR 2158/1-1 and the Eu- ropean Commission within FET Open Project COSIN (IST-2001-33555). c visone logos by Christiane N˜ostlinger and Ulrik Brandes. 2 Ulrik Brandes and Dorothea Wagner a standard tool with all due consequences such as extensive user-support and product marketing. Essentially all components are in development and therefore subject to change. In a nutshell, visone is a † tool for interactive analysis and visualization of networks, in which † originality is preferred over comprehensiveness, and that † caters especially to social scientists. The organization of the subsequent sections follows the common structure of all chapters in this book. In particular, we start with background information on the main area of application for visone, and give application examples in Section 5. While other interesting algorithms have been implemented, Section 3 focusses on those for graph drawing. 2 Applications Thema ina pplication area of visone isamethodologicalapproachinthesocial sciences: Social Network Analysis uses graph-theoretic concepts to describe, understand and explain, sometimes even predict or design, social structure. The objects of interest are emergent patterns of relationships and their in-...

Website: www.informatik.uni-konstanz.de | Filesize: -
No of Page(s): 20
Download Analysis and Visualization of Social Networks⋆ 1 Introduction.pdf

Methods of Semantic Drift Reduction in Large Similarity Networks

We have investigated the problem of clustering documents according to their semantics, given incomplete and incoherent hints reflecting the documents’ affinities. The problem has been rigorously defined using graph theory in set-theoretic notation. We have proved the problem to be NP-hard, and proposed five heuristic algorithms which deal with the problem using five quite different approaches: a greedy algorithm, an iterated finding of maximum cliques, energy minimization inspired by molecular me- chanics, a genetic algorithm, and an adaptation of the Girvan-Newman algorithm. As

a side effect of the fourth heuristic, an efficient and aesthetically appealing method of visualization of the large graphs in question has been developed. The approaches have been tested empirically on the network of links between arti- cles from over 250 language editions of Wikipedia. A thorough analysis of the network has been performed, showing surprisingly large semantic drift patterns and an uncom- mon topology: a scale-free skeleton linking tight clusters. It has been demonstrated that, using a blend of the proposed approaches, it is possible to automatically detect, and to a large extent eliminate, the semantic drift in the network of links between the language editions of Wikipedia. Last but not least, an open-source implementation of the proposed algorithms has been documented. To my wife Duygu and my son Leon Contents 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Notation and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.4 Key Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.5 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2 Literature Review and State of the Art 15 2.1 Computational Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Models of Network Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Power-Law Distributions . . . . ....

Website: www.icm.edu.pl | Filesize: -
No of Page(s): 102
Download Methods of Semantic Drift Reduction in Large Similarity Networks.pdf

Mobility model based on social network theory

Vehicular Ad-Hoc Networks (VANET) are a particular type of wireless ad- hoc networks. They are formed when equipping vehicles on the roads with short range wireless communication devices. Validation of mobile ad hoc network protocols relies almost exclusively on simulation. The value of the validation is, therefore, highly dependent on how realistic the movement models used in the simulations are. However, most widely used models are currently very simplistic, their focus being

the ease of implementation rather than soundness of foundation. As a consequence, simulation results of protocols are often based on randomly generated movement patterns and, therefore, may differ considerably from those that can be obtained by deploying the system in real scenarios. In this we propose a new mobility model based on the social network theory. The mobility model creates movement pattern by taking into consideration the social relationship among the individuals, social relationship that might change depending on the simulation time. We also present the results obtained in validating our model using the realistic vehicular traces designed at the ETH Zurich institute. Ana Gainaru - Model de mobilitate bazat pe retele sociale pentru retele VANET 3 Table of contents 1. INTRODUCTION ................................................................................................ 4 2. RELATED WORK ............................................................................................... 7 3. MOBILITY MODELS ........................................................................................... 9 3.1. Classification ................................................................................................ 9 3.1.1. Synthetic Models ................................................................................. 10 3.1.2. Traffic Simulator-based Models ........................................................... 10 3.1.3. Survey-based Models .......................................................................... 11 3.1.4. Trace-based Models ............................................................................ 12 3.2. Maps.......................................................................................................... 12 4. THE VNSim SIMULATOR................................................................................. 14 4.1. Maps........................................................................................................... 14 4.2. The traffic simulator.................................................................................... 15 4.3. Implementation Details of the Mobility Model ............................................. 16 4.3.1. Scenarios Generation .......................................................................... 16 4.3.2. The motion model ................................................................................ 20 5. MOBILITY MODEL BASED ON SOCIAL NETWORKS ................................... 22 5.1. Realistic Vehicular Traces.......................................................................... 23 5.1.1. The ETH Zurich traces......................................................................... 23 5.1.2. Implementation details......................................................................... 24 5.2. Social networks .......................................................................................... 25 5.2.1. The social mobility model..................................................................... 25 5.2.2. Overview.............................................................................................. 26 5.2.3. Input information. ................................................................................. 32 5.2.4. Configuration algorithm........................................................................ 33 5.2.5. The routing algorithm.......................................................................... 42 5.2.6. Object design....................................................................................... 45 6. EVALUATION RESULTS.................................................................................. 47 6.1. Degree of connectivity................................................................................ 49 6.2. Inter-contacts time...................................................................................... 51 6.3. Contact duration ......................................................................................... 53 6.4. The influence of the population density on the inter-contact time and contacts duration............................................................................................... 54 6.5. Vehicle density in intersections .................................................................. 56 7. CONCLUSIONS AND FUTURE WORK ........................................................... 58 REFERENCES .................................................................................................................. 59 Ana Gainaru - Model de mobilitate bazat pe retele sociale pentru retele VANET 4 1. INTRODUCTION Vehicle-to-vehicle communication is a concept greatly studied during the past years. Vehicles equipped with devices capable of short-range wireless connectivity can form a particular mobile ad-hoc network, called a “Vehicular Ad- hoc NETwork” (or VANET). The users of a VANET, drivers or passengers, can be provided with useful information and with a wide range of interesting services. There are 3 types of applications developed in VANETs: route planning applications, safety-related applications and commercial applications. The first type of application consists of gathering real-time...

Website: cipsm.hpc.pub.ro | Filesize: -
No of Page(s): 60
Download GRADUATION PROJECT Mobility model based on social ... - Home.pdf

Analysing Social Networks Via the Internet

The purpose of this article is to introduce the reader tothe history, concepts, measures and methods of social network analysis as applied to online information spaces. This is done through description as well as a sustained example using the online social news site Digg.com. Social network analysis is a rapidly expanding interdisciplinary paradigm , much of which is taking place with online data. As such, some concepts will only be addressed superficially, while others (such as positions, p* models and multilevel analysis) will be excluded entirely. The goal is to facilitate enough network literacy to begin a research project rather than provide a complete end-to-end solution. Social network analysis has

emerged in the past half-century as a compelling complement to the standard toolkit of social science researchers. At its foundation is a belief that explanations for social organization are not to be found in innate drives or abstract forces. Instead we can look to the structure of relationships that constrain and enable interaction (Wellman, 1988) alongside the behaviors of agents that reproduce and alter these structures (Emirbayer & Mische, 1998). While this paradigm has been applied to fields as diverse as sexual contacts among adolescents (Bearman, Moody, & Stovel, 2004) and intravenous drug users (Koester, Glanz, & Baron, 2005), social network analysis is particularly well suited to understanding online interaction. There are two key facts about online interaction that make it particularly amenable to social network analysis - the nature of online interaction and the nature of digital information. Online interaction is almost always social network-oriented. At its simplest, social networks refer to a series of nodes (such as people, organizations or web pages) and the specific links between two of these nodes. Hypertext (such as the World Wide Web) is an unstructured series of pages and links between pages. Communication online can be represented as a network of senders and recipients. Finally, relationships on social software sites constitute an obvious series of nodes (profiles) and links (friends). As Barry Wellman muses, “when computer networks link people as well as machines they become social networks” (1996, p. 214). While digital information does not have to be network- oriented, this certainly facilitates the capture of network data. Granted, communication patterns and relationships were stud- ied as networks long before the internet. However, collecting in-person data is time consuming and difficult; people are sometimes unclear of who is in their personal network (or how strong the tie is), and it is important to gather high response rates. These problems can be minimized online because information is digital and encoded merely through the act of sending a message or adding a friend to one’s page. Also, there is virtually no marginal cost in making a perfect replica of the messages for analysis. II. THE FUNDAMENTALS OF SOCIAL NETWORKS A. Social networks in historical context The roots of social network analysis are found in the math- ematical study of graph theory (such as the work of Erdos, Harary and Rappaport) and empirical studies of social psy- chology (such Bott, Heider and Moreno)1. While the...

Website: individual.utoronto.ca | Filesize: -
No of Page(s): 13
Download Analysing Social Networks Via the Internet.pdf

Community Detection Methods based on Network Communicability

We propose a series of methods for detecting communities based on the concept of communicability between nodes in a complex network. These method are based on a relaxed definition of community C as a subset of nodes for which the intracluster communicability is larger than the intercluster one for most, but not necessarily all, of the pairs of nodes in C . The first method proposed is a modification of the Girvan-Newman algorithm that considers the communicability graph associated to a network instead of the network itself. The communicability graph

of a network is build using the same set of nodes but connecting two nodes if, and only if they have larger intra- than intercluster communicability. Two other methods are proposed on the basis of the communicability graph. They are based on similarity techniques using the adjacency and distance matrices of the communicability graph. A fourth method uses a real-value communicability matrix of a network as a similarity between nodes. In general, we have shown that the communicability-based techniques produce partitions with better modularity than the classical algorithms. In particular, the method using the communicability matrix produces the best results reported so far for partitioning two classically used networks with known partitions. Communities play fundamental organizational and functional roles in networks representing complex systems. Most of the algorithms used to detect these structures use information directly contained in the topology of these networks, such as adjacency and distance relationships. This paper proposes a series of new techniques for identifying communities using the concept of network communicability, which is based on walks on networks. Nodes are grouped into communities according to their capacity of communicating better among them than with outsiders. One of these methods, which build the similarity between nodes on the basis of the communicability matrix of the network, produces partitions displaying the highest modularity reported so far for some real-world networks with known community structures. I. INTRODUCTION Complex networks are the structural skeleton of complex systems, which are ubiquitous 1-3 in nature, society and technology. A network is represented by a graph, G = ( V , E ) , where the set of nodes V represents the entities of the system and the set of links E represents the 2 (binary) relationship between these entities. A ‘microscopic’ analysis of a complex network is possible by considering its local topological properties, i.e., those derived from the analysis of close environments around individual nodes and links. Some examples of these local 4 properties are those of centrality, such as degree, closeness, betweenness, etc., or network 5 6 motifs and graphlets, which are small subgraphs centred at a given node. On the other side of the scale we can study some ‘macroscopic’ properties of these complex networks by analyzing their global topological properties. Some examples of these global properties are 7 8 9 10 degree distributions, ‘small-worldness’, self-similarity, good expansion properties, etc. However, a closer inspection of the structure of...

Website: www.mathstat.strath.ac.uk | Filesize: -
No of Page(s): 35
Source to download Community Detection Methods based on Network Communicability.

Identifying and evaluating community structure in complex networks

Scalability different to community comp d with problems arising Recently, datasets that can networks, have received (Girvan interactions 2002 and many Of course this method is infeasible for networks larger than a handful of nodes, prompting the development of automated detec- tion techniques. The formulation of an algorithm and, more impor- tantly, the validation of its output requires a more concise definition of a community. Newman and Girvan (2004) were among the first to address this issue and proposed modularity to quantify the strength of community structure. This metric, based on the intuition that nodes within the same community should be more tightly connected than they would be by chance, has been adopted for a variety of uses including the

validation and compar- ison of community structures (Newman and Girvan, 2004; Pons and Latapy, 2006), but also as an objective function for optimiza- tion algorithms to identify communities (Clauset et al., 2004; Do- are often confounded by large networks and become fragile as datasets approach 10 5 –10 6 (or more) nodes. We believe that in or- der to achieve this level of scalability, a method much simpler than an optimization algorithm must be employed. To this end, we out- line an intuitive approach to community detection based on random walks and compare it to several published algorithms using a variety of metrics. Our experimental results show that this simple method is as good or better at discovering the true communities than other more complex algorithms. Finally, we discuss several possible exten- sions to the approach, and demonstrate its scalability on a network of over 1 million nodes, where other methods falter. * Corresponding author. Tel.: +1 574 631 8716; fax: +1 574 631 9260. Pattern Recognition Letters xxx (2009) xxx–xxx Contents lists available nition ARTICLE IN PRESS E-mail address: nchawla@nd.edu (N.V. Chawla). increasing availability of rich network data, there is also a need for effective and efficient analysis methods. One problem of great interest for pattern recognition in com- plex networks is community detection, or the unsupervised discov- ery of densely connected subgroups which are known to exist in many real-world networks. On the surface the concept of commu- nities appears intuitive, and if properly arranged their structure can be identified by visual inspection as illustrated in Fig. 1. ularity does not necessarily coincide with the correct division of the network; in this case algorithms that maximize modularity con- verge on a suboptimal solution, that is, miss the discovery of the actual and meaningful communities. We demonstrate this using a variety of metrics on diverse datasets for which the actual communi- ties are known as ground truth. Another issue with community detection algorithms is their computational complexity. These methods, rooted in graph theory, Evaluation metrics 1. Introduction Modern data mining is often confronte from complex relationships in data. be represented as graphs, or interaction considerable attention in various domains. clude the analysis of social networks Wasserman and Faust, 1994), chemical teins (Asur et al., 2007; Enright et al., and services (Clauset et al., 2004), 0167-8655/$ - see front matter C211 2009 Elsevier B.V. All doi:10.1016/j.patrec.2009.11.001 Please cite this article...

Website: icensa.nd.edu | Filesize: -
No of Page(s): 9
Download Identifying and evaluating community structure in complex networks.pdf

Network Structure of the Web

Network Structure of the Web Part 2 Web Surfing as a Markov Chain Process, continued • Adjacency Matrix A: – If there is a hyperlink from page i to page j, then Aij = 1, otherwise Aij = 0. • Transition Matrix P: – If a row of A has no 1s (i.e., no out-links), then insert 1/N for each element in that row in P (uniform teleporting probability) – Otherwise, divide each 1 in the row in A by the number of 1s in its row. (uniform probability of going to out-link) – Multiply the resulting matrix by (1- α) (probability of going to that linked page by not teleporting) – Add α /N to every entry

of the resulting matrix (probability of going to that each by teleporting) 6/3/2010 2 • Exercise 21.6: Consider the following web graph. What are the transition matrices for α = 0 and 0.5? 1 2 3 Computing PageRank, continued • Suppose alpha = 0.5. Let xt be the probability distribution over the states at time t. Suppose surfer starts in state 1. I.e., x0 = (1 0 0). After one time step, we have x1 = x0 P = (1/6 2/3 1/6) After two time steps, x2 = x1 P = (1/3 1/3 1/3). Keep going. Finally reach steady state of (5/18 4/9 5/18). [Show this is a steady state] 1 2 3           = 6/13/26/1 12/56/112/5 6/13/26/1 P 6/3/2010 3 Questions • What is the minimum possible PageRank of a page? • How does varying α affect PageRank? From http://www.geek.com/articles/chips/googles-pagerank-algorithm-traced- back-to-the-1940s-20100217/ Earlier forerunner to PageRank in the work of the Harvard economist Wassily Leontief: “In 1941, Leontief published a paper in which he divides a country's economy into sectors that both supply and receive resources from each other, although not in equal measure. One important question is: what is the value of each sector when they are so tightly integrated? Leontief's answer was to develop an iterative method of valuing each sector based on the importance of the sectors that supply it. Sound familiar? In 1973, Leontief was awarded the Nobel Prize in economics for this work.” 6/3/2010 4 Other Uses for Page Rank • Ranking journal impact (nodes are journals, links are citations in articles in one journal to articles in the other journal -- e.g., see http://www.eigenfactor.org) • Ranking doctoral programs (departments are nodes, one node links to another if it hires faculty from that dept.) • Food webs – species that are essential to an ecosystem 6/3/2010 5 “Here we show that an algorithm adapted from the one Google uses to rank web-pages can order species according to their importance for coextinctions, providing the sequence of losses that results in the fastest collapse of the network.” Hubs and Authorities (HITS Algorithm) • Proposed by Jon Kleinberg (Cornell) at same time Brin and Page were developing PageRank • HITS: “Hyperlinked-induced topic search” • Supposedly used by Teoma and Ask.com 6/3/2010 6 Hubs and Authorities Main ideas Each node has a hub score and an authority score Hub: Web...

Website: web.cecs.pdx.edu | Filesize: -
No of Page(s): 22
Download Network Structure of the Web.pdf

Using Complex Network Features for Fast Clustering in the Web

Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter

estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency. Categories and Subject Descriptors H.3.3 [Information Systems]: Information Search and Retrieval - Clustering; General Terms Algorithms, Performance, Experimentation Keywords Graph clustering, complex networks, parameter estimation. 1. INTRODUCTION Graph clustering is an important technology in social network analysis. With the rapid development of the Internet, network applications must deal with very large networks, often comprising millions of nodes. How to find a good tradeoff between speed and accuracy becomes an open challenge. Furthermore, how to discover a priori knowledge of real-world networks to determine parameters is crucial to the performance of clustering algorithms. Most real-world networks are complex networks which have some topological features that are not apparent in simple networks. These topological features reveal additional information of the communities in complex networks. For instance, the small world [1] hypothesis suggests that the diameter of the complex networks is small; the high clustering coefficient [2] suggests that most of the nodes are only connected to some neighbors in the same cluster; the low diameter mainly depends on a few “weak long ties” among the clusters. Scale-free feature [3] indicates that the distribution of degrees follow a power-law to suggests that a few active nodes consume a lot of edges, and other common nodes only consume very few edges. From these works in complex networks we can deduce two hypothesis: (1) most of the common nodes are connected to its neighbors to form some clusters towards the so called active nodes, and (2) a few weak ties among clusters make greater contribution to network connections. In this paper, we employ the hypothesis to optimize the graph clustering algorithms for large-scale complex networks such as link-based data in the web. The first hypothesis is also used to determine clustering parameters. Two well-known algorithms are used to evaluate the performance of the proposed methods. The selected algorithms include the k-medoids algorithm [4] and the Girvan-Newman algorithm [5], which are widely studied in social networks analysis. Experiments show that the performance of clustering algorithms is improved by approximating the shortest paths algorithm based on the hypothesis. 2. COMPLEX NETWORK CLUSTERING Many graph-clustering algorithms are time-consuming because of the bottleneck of all-pairs shortest paths problem (APSP). Since the aforementioned hypothesis says that the...

Website: www2011india.com | Filesize: -
No of Page(s): 2
Download Using Complex Network Features for Fast Clustering in the Web.pdf

Communities in Complex Networks

Communities in Complex Networks: Identification at Different Levels - Alex Arenas, Jordi Duch, Sergi Gómez, Leon Danon, Albert Díaz-Guilera ©Encyclopedia of Life Support Systems (EOLSS) COMMUNITIES IN COMPLEX NETWORKS: IDENTIFICATION AT DIFFERENT LEVELS Alex Arenas, Jordi Duch and Sergi Gómez Departament Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Spain Leon Danon Mathematics Institute, University of Warwick, Great Britain Albert Díaz-Guilera Departament Física Fonamental, Universitat de Barcelona, Spain Keywords: Communities, hierarchies, overlap, dynamics Contents 1. Introduction 2. Definition of communities 3. Evaluating community identification 4. Link removal methods 4.1. Shortest Path Centrality 4.2. Extensions of the Shortest Path Centrality 4.3. Information Centrality 4.4. Link Clustering 5. Agglomerative methods 5.1. Hierarchical Clustering 5.2.

L-Shell Method 5.3. K-Clique Method 6. Maximizing modularity methods 6.1. Greedy Algorithm 6.2. Extremal Optimization 6.3. Simulated Annealing Methods 6.4. Information Theoretic Approach 7. Spectral Analysis methods 7.1. Spectral Bisection 7.2. Multi Dimensional Spectral Analysis 7.3. Constrained Optimization 7.4. Approximate Resistance Networks 8. Other methods 8.1. Clustering and Curvature 8.2. Random Walk Based Methods 8.3. Q-Potts Model 9. Further structural complexity 9.1. Hierarchical Organization 9.2. Overlap 10. Applications: Search and congestion 11. Conclusions UNESCO – EOLSS SAMPLE CHAPTERS COMPLEX NETWORKS - Communities in Complex Networks: Identification at Different Levels - Alex Arenas, Jordi Duch, Sergi Gómez, Leon Danon, Albert Díaz-Guilera ©Encyclopedia of Life Support Systems (EOLSS) Acknowledgements Glossary Bibliography Biographical Sketches Summary We present here and compare the most common approaches to community structure identification in terms of sensitivity and computational cost. The work is intended as an introduction as well as a proposal for a standard benchmark test of community detection methods. 1. Introduction The analysis of complex networks has received a vast amount of attention from the scientific community during the last decade. Statistical physicists in particular have become interested in the study of networks describing the topologies of a wide variety of systems, from biological technological or social networks. Although several questions have been addressed (see the review paper by Costa et al. for a complete set of measurements), many important ones still resist complete resolution. One such problem is the analysis of modular structure found in many networks. Distinct modules or communities within networks can loosely be defined as subsets of nodes which are more densely linked, when compared to the rest of the network. Such communities, as usually called in social sciences, have been observed, using some of the methods we shall go on to describe, in many different contexts, including biological networks, economic networks and most notably social networks. As a result, the problem of identification of communities has been the focus of many recent efforts. As a concrete example we show in Figure 1 the network representing the Spanish research community of Statistical and Nonlinear Physicists (FISES, http://www.fises.es). We consider two scientists linked if they have co-authored a panel contribution to any of the conferences. To be able to consider the historical structure of this network we ''accumulate'' the network over all the conferences, that is, once a link is created, it remains, even if the authors never collaborated again. The final network...

Website: www.eolss.net | Filesize: -
No of Page(s): 11
Download Communities in Complex Networks: Identification at ... - EOLSS.pdf

Using FPGA-Based Channel Bonding for HDTV Over DSL

On an almost daily basis, new video or voice applications push the bandwidth requirements for DSL networks, while telecom carriers in the U.S. and worldwide are targeting delivery of digital and high-definition television (HDTV) to consumers. To achieve delivery of such services without deploying new fiber everywhere, carriers must leverage existing copper deployments already in the ground.

Most DSL lines offer enough capacity for delivering standard-definition television (SDTV). Most programs are available from streaming servers at bitrates of about 750 kbps, with some programs providing a 1.5 Mbps bitrate. However, to allow high-quality HDTV streaming and multiple channels simultaneously, a home must have a bandwidth of at least 16 Mbps. Although newer DSL generations of ADSL2 and VDSL can offer these speeds, they cannot offer high speed over a sufficiently longer distance on a typical DSL line. Therefore, HDTV programs can be delivered only to households close to the DSLAM. Those located further away can only receive lower quality SDTV programming.

To ensure that DSL remains the preferred choice for end users, service providers are looking for new ways to improve the performance of DSL networks. While VDSL and ADSL2 provide better performance, the distance limitations are difficult to overcome. Another scenario is to bring the DSLAMs closer to the end users, but the costs involved with installing new equipment in the network are often prohibitive.

Channel Bonding in DSLAMs and DSL Modems
DSL channel bonding provides the ideal mix of features: higher bandwidth to all users and the ability to extend the distance that can be reached at a certain bandwidth. Instead of using a single copper pair, DSL bonding distributes traffic over a bundle of copper pairs. To achieve an effective bandwidth of 12 Mbps, three DSL lines of 4 Mbps are bundled, with a channel bonding processor at each end of the lines. In most copper networks, subscribers are already connected via several wires, so no new cables need be installed to provide channel bonding service, as shown in Figure 1.

Download pdf Using FPGA-Based Channel Bonding for HDTV Over DSL

Wireless HDTV – Compressed or Uncompressed?

Wireless HDTV continues to be a hot topic in the consumer electronics space. The need for a solution that will finally eliminate audio/video wires is stronger than ever. The TV market is at an inflection point ready to take off, propelled by a combination of major technical and regulatory advances. Flat panel display, LCD and plasma technologies have enabled an amazing offering of elegant TVs that most people want in their living room. HD content is also fueling the demand for HDTVs, with most consumers in the US and Japan having access to a wide array of HD content from TV networks and cable channels, and distributed via terrestrial, cable or satellite broadcasts. In the US this trend is facilitated by the FCC which is making sure through regulation and its influence on cable/satellite operators that HDTV is finally going to happen and on a large scale. Other world markets will follow, including Europe, which already has several satellite providers offering HD programming.

Sporting events such as the Super-Bowl or the Olympic Games see more people rushing to spend thousands of dollars on new HDTV sets. The availability of new HD DVDs will only intensify this demand. This hot market is attracting new players from the PC space such as HP and Dell who hope to take a slice of the TV market from the incumbent TV brands. With such intense competition in this lucrative market, CE manufacturers are investing heavily in differentiating qualities enabling them to offer more elegant designs, better picture quality and more functions. A wireless interface would be a perfect addition to their offerings.

Consumers have shown that they like wireless. The proliferation of cordless phones, Bluetooth headsets and Wi-Fi home networking kits are just a few indications of this preference. Consumers are very likely to opt for a TV with a wireless interface over a TV without one. What is the point of spending so much money on an elegant wall-hanging flat panel TV if its aesthetic appeal is compromised by wires running to the display? To illustrate this concern, one TV manufacturer tells a story about a couple at an electronics store where the wife says: “OK, you can have your silly four- thousand dollar TV, but I don’t want to see any wires running through our living room…”

The need for wireless HDTV is even stronger when it comes to multimedia projectors. The market for HDTV multimedia projectors for home use is growing dramatically. A true cinema experience with a huge picture cannot be matched by TV sets, and the space occupied by these machines is very small. In many cases a projector is not purchased in place of a TV but rather as a complement to it; to be used for special events such as parties and other social gatherings or a ‘night out’ at the home cinema. Although growth is strong, this market is very far from realizing its potential. Perhaps the greatest inhibitor of further growth is the installation difficulty. Having to run video wires across the room to the projector discourages many from purchasing this device. The high prices – as much as several hundreds of dollars – of the long video cables required for projector installation, make the installation experience even more painful. A wireless interface would make all the difference.

It is not surprising therefore, that so many companies have been trying to address this need. Many top TV OEMs have been spending resources on wireless TV technology, while standard bodies and special interest groups, such as 802.11n and UWB, are also targeting this application. Most of the solutions that have been proposed for wireless HDTV share a common assumption: the HD video stream delivered wirelessly is compressed with a typical data rate of 10-30 Mbps. This assumption is based on the premise that video is distributed to the home through terrestrial, cable or satellite...

Get pdf download Wireless HDTV – Compressed or Uncompressed?

Experiments with Delivery of HDTV over IP Networks

The conversion of broadcast television from the legacy analog PAL and NTSC standards to digital format has many exciting implications. These include the possible convergence of television distribution and computer network infrastructures, allowing interactive applications, and the increase in quality possible with high definition digital formats.

To date, the different aspects of this convergence have been studied in isolation: there has been much work on the transport of compressed standard definition TV over IP, and much work defining protocols and standards for high definition TV (HDTV), but few have studied the transport of HDTV over IP. In this paper we present our initial experiments with a system to deliver production quality uncompressed HDTV over IP networks.

Why do we chose to deliver uncompressed HDTV? Several reasons, primarily to maintain image quality and reduce latency. This is most useful in a production facility, where image degradation due to repeated compression cycles is undesirable, but may also be appropriate for very high quality telepresence applications. Delivery of compressed HDTV, using existing MPEG-2 over IP standards, may be more appropriate for other applications.

The outline of this paper is as follows: section 2 covers background in HDTV technology, protocols for transport of video over IP networks and network performance. This is followed, in section 3 with a discussion of the options for protocol development, with our design being outlined in section 4. Section 5 provides preliminary performance analysis of our system, demonstrating transmission of HDTV over a wide-area IP network, with section 6 outlining directions for further development. Finally, we summarize related work in section 7, and provide conclusions.

Get pdf download Experiments with Delivery of HDTV over IP Networks

Guide to Getting Connected

Step 1: Ensure that your wireless device has a compatible wireless adapter.
Ensure that your wireless device, e.g., laptop computer, Personal Digital Assistant (PDA), mobile phone, etc. has an IEEE 802.11b/g (Wi-Fi) compliant network adapter. Most newer wireless devices have built-in wireless adapters. If your wireless device does not come with a Wi-Fi network adapter, you will need to purchase a separate adapter. Below are some examples of external Wi-Fi adapters.

Please check with your device manufacturer the correct type of Wi-Fi adapter to purchase. If you have an external adapter, ensure that the driver for the adapter is properly installed before proceeding with the above steps. Please refer to the user manual and software that comes with the external wireless adapter for the instructions. Ensure that the driver and related software is working before proceeding.

Step 2: Verify that your wireless adapter is turned on.
For laptops with built-in wireless adapters, ensure that the wireless switch that is usually physically located on the front or side of the laptop is turned on.

Download pdf Guide to Getting Connected

VIPRE Enterprise Quick Start Guide

VIPRE Enterprise (VPE) is an enterprise application that is installed on a server and referred to as the VPE server. The VPE server deploys Agents on to your network workstations. Administrators use the policy-based, centrally-managed Admin Console to manage and remove viruses and a broad range of malware from the network. VPE is also a scalable solution, appropriate for both small and large organizations.

This Quick Start Guide is designed to give you a basic understanding of the tasks necessary for implementing VPE.

The VPE Server should be installed on a Windows 2000, 2003, or 2008 server. It is possible to install the VPE Server on a Windows 2000/XP/Vista workstation, but this is not recommended if you plan to deploy more than 50 agents.

Download pdf VIPRE Enterprise Quick Start Guide

ResNet Information Guide

This guide is intended to provide you with detailed information about the on-campus residential computing network. It is written for students planning to reside on campus. It is strongly recommended that you read this document in its entirety. Doing so will more than likely save you time, money and effort once you arrive on campus (not to mention, it will make you feel a little bit cooler). We encourage parents to read through this guide, too. However, parents, please be sure that your student reads this as some details explain what they will need to do on a daily basis to gain network access.

We expect that you will come to a complete understanding of the information contained in this guide. If you have any questions, please address them prior to arriving. You can find our contact information on our “Contact Us” page.

In addition to the information contained in this document, Cal Poly's Information Technology Services website contains a plethora of additional computing information. Much of this information applies to student accounts and on-campus computing. These documents outline policies that hold true across the entire campus, including on the residence hall network. You should read through all the legal stuff contained on their website. We welcome and congratulate you on your acceptance to Cal Poly!

Download pdf ResNet Information Guide

NVivo 8 Network Administrator’s Guide

Videos. Interview recordings. PDF documents. Word documents. Photos. Media clips. Music. Whatever the materials, whatever the language, whatever the project—NVivo 8 lets users explore and analyze their information like never before. It has a wide range of tools to make working in teams more effective, the ability to produce models and charts, and the unique capability to export information as HTML web pages.

Hardware and software requirements
Installing NVivo 8 is a simple process that involves moving through a series of screens. Before installing the software, make sure that your computer meets the hardware and software requirements described below. It is also a good idea to have your license key nearby. You can find this on your NVivo CD sleeve, or—if you downloaded the product—in the download email communication received from QSR.

Download pdf NVivo 8 Network Administrator’s Guide