scholarly journals Using Power-Law Degree Distribution to Accelerate PageRank

2012 ◽  
Vol 1 (2) ◽  
pp. 63-70
Author(s):  
Zhaoyan Jin ◽  
Quanyuan Wu
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
Real World ◽  
Degree Distribution ◽  
World Wide ◽  
Power Law Distribution ◽  
The World ◽  
Real World Applications ◽  
Real World Datasets ◽  
Law Degree

The PageRank vector of a network is very important, for it can reflect the importance of a Web page in the World Wide Web, or of a people in a social network. However, with the growth of the World Wide Web and social networks, it needs more and more time to compute the PageRank vector of a network. In many real-world applications, the degree and PageRank distributions of these complex networks conform to the Power-Law distribution. This paper utilizes the degree distribution of a network to initialize its PageRank vector, and presents a Power-Law degree distribution accelerating algorithm of PageRank computation. Experiments on four real-world datasets show that the proposed algorithm converges more quickly than the original PageRank algorithm.DOI: 10.18495/comengapp.12.063070

Priority Weighted Fitness Model in Networks

2012 ◽  
Vol 229-231 ◽  
pp. 1854-1857
Author(s):  
Xin Yi Chen
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
Common Property ◽  
World Wide ◽  
Genetic Networks ◽  
Power Law Distribution ◽  
Large Networks ◽  
Scale Free ◽  
The World ◽  
Complex Topology

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a power-law distribution. This feature was found to be a consequence of three generic mechanisms: (i) networks expand continuously by the addition of new vertices, (ii) new vertex with priority selected different edges of weighted selected that connected to different vertices in the system, and (iii) by the fitness probability that a new vertices attach preferentially to sites that are already well connected. A model based on these ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena. Experiment results show that the model is more close to the actual Internet network.

Construction of Domain Ontologies

2011 ◽  
Vol 7 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Jongwoo Kim ◽  
Veda C. Storey
Keyword(s):  
World Wide Web ◽  
Semantic Web ◽  
Empirical Analysis ◽  
Real World ◽  
World Wide ◽  
Ontology Development ◽  
Application Domain ◽  
The World ◽  
Real World Applications ◽  
Domain Ontologies

As the World Wide Web evolves into the Semantic Web, domain ontologies, which represent the concepts of an application domain and their associated relationships, have become increasingly important as surrogates for capturing and representing the semantics of real world applications. Much ontology development remains manual and is both difficult and time-consuming. This research presents a methodology for semi-automatically generating domain ontologies from extracted information on the World Wide Web. The methodology is implemented in a prototype that integrates existing ontology and web organization tools. The prototype is used to develop ontologies for different application domains, and an empirical analysis carried out to demonstrate the feasibility of the research.

Construction of Domain Ontologies

2012 ◽  
pp. 65-87
Author(s):  
Jongwoo Kim ◽  
Veda C. Storey
Keyword(s):  
World Wide Web ◽  
Semantic Web ◽  
Empirical Analysis ◽  
Real World ◽  
World Wide ◽  
Ontology Development ◽  
Application Domain ◽  
The World ◽  
Real World Applications ◽  
Domain Ontologies

As the World Wide Web evolves into the Semantic Web, domain ontologies, which represent the concepts of an application domain and their associated relationships, have become increasingly important as surrogates for capturing and representing the semantics of real world applications. Much ontology development remains manual and is both difficult and time-consuming. This research presents a methodology for semi-automatically generating domain ontologies from extracted information on the World Wide Web. The methodology is implemented in a prototype that integrates existing ontology and web organization tools. The prototype is used to develop ontologies for different application domains, and an empirical analysis carried out to demonstrate the feasibility of the research.

Power-Law Distribution of the World Wide Web

Science ◽  
2000 ◽  
Vol 287 (5461) ◽  
pp. 2115 ◽  
Author(s):  
Lada A. Adamic ◽  
Bernardo A. Huberman ◽  
A.-L. Barabási ◽  
R. Albert ◽  
H. Jeong ◽  
...  
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
World Wide ◽  
Power Law Distribution ◽  
The World

scholarly journals A Simulation of the Structure of the World-Wide Web

2002 ◽  
Vol 7 (1) ◽  
pp. 9-25 ◽  
Author(s):  
Moses Boudourides ◽  
Gerasimos Antypas
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
Web Sites ◽  
World Wide ◽  
The Internet ◽  
Web Pages ◽  
Small Worlds ◽  
Web Page ◽  
Simple Simulation ◽  
The World

In this paper we are presenting a simple simulation of the Internet World-Wide Web, where one observes the appearance of web pages belonging to different web sites, covering a number of different thematic topics and possessing links to other web pages. The goal of our simulation is to reproduce the form of the observed World-Wide Web and of its growth, using a small number of simple assumptions. In our simulation, existing web pages may generate new ones as follows: First, each web page is equipped with a topic concerning its contents. Second, links between web pages are established according to common topics. Next, new web pages may be randomly generated and subsequently they might be equipped with a topic and be assigned to web sites. By repeated iterations of these rules, our simulation appears to exhibit the observed structure of the World-Wide Web and, in particular, a power law type of growth. In order to visualise the network of web pages, we have followed N. Gilbert's (1997) methodology of scientometric simulation, assuming that web pages can be represented by points in the plane. Furthermore, the simulated graph is found to possess the property of small worlds, as it is the case with a large number of other complex networks.

scholarly journals Specialization Models of Network Growth

2018 ◽  
Vol 7 (3) ◽  
pp. 375-392 ◽  
Author(s):  
L A Bunimovich ◽  
D C Smith ◽  
B Z Webb
Keyword(s):  
Power Law ◽  
Network Topology ◽  
Real World ◽  
Degree Distribution ◽  
Small World ◽  
Hierarchical Network ◽  
Network Growth ◽  
Complex Tasks ◽  
Small World Property ◽  
Law Degree

AbstractOne of the most important features observed in real networks is that, as a network’s topology evolves so does the network’s ability to perform various complex tasks. To explain this, it has also been observed that as a network grows certain subnetworks begin to specialize the function(s) they perform. Herein, we introduce a class of models of network growth based on this notion of specialization and show that as a network is specialized using this method its topology becomes increasingly sparse, modular and hierarchical, each of which are important properties observed in real networks. This procedure is also highly flexible in that a network can be specialized over any subset of its elements. This flexibility allows those studying specific networks the ability to search for mechanisms that describe their growth. For example, we find that by randomly selecting these elements a network’s topology acquires some of the most well-known properties of real networks including the small-world property, disassortativity and a right-skewed degree distribution. Beyond this, we show how this model can be used to generate networks with real-world like clustering coefficients and power-law degree distributions, respectively. As far as the authors know, this is the first such class of models that can create an increasingly modular and hierarchical network topology with these properties.

scholarly journals Width of a scale-free tree

2005 ◽  
Vol 42 (03) ◽  
pp. 839-850 ◽  
Author(s):  
Zsolt Katona
Keyword(s):  
Theoretical Result ◽  
Power Law ◽  
Real World ◽  
Degree Distribution ◽  
Graph Model ◽  
Random Graph Model ◽  
Scale Free ◽  
Slight Generalization ◽  
Free Tree ◽  
Law Degree

Consider the random graph model of Barabási and Albert, where we add a new vertex in every step and connect it to some old vertices with probabilities proportional to their degrees. If we connect it to only one of the old vertices then this will be a tree. These graphs have been shown to have a power-law degree distribution, the same as that observed in some large real-world networks. We are interested in the width of the tree and we show that it is at the nth step; this also holds for a slight generalization of the model with another constant. We then see how this theoretical result can be applied to directory trees.

scholarly journals Preprocessing Rules for Target Set Selection in Complex Networks

2020 ◽  
Author(s):  
Renato Silva Melo ◽  
André Luís Vignatti
Keyword(s):  
Complex Networks ◽  
Power Law ◽  
Real World ◽  
Degree Distribution ◽  
Topological Properties ◽  
Input Graph ◽  
Target Set ◽  
Law Degree ◽  
Entire Network ◽  
Target Set Selection

In the Target Set Selection (TSS) problem, we want to find the minimum set of individuals in a network to spread information across the entire network. This problem is NP-hard, so find good strategies to deal with it, even for a particular case, is something of interest. We introduce preprocessing rules that allow reducing the size of the input without losing the optimality of the solution when the input graph is a complex network. Such type of network has a set of topological properties that commonly occurs in graphs that model real systems. We present computational experiments with real-world complex networks and synthetic power law graphs. Our strategies do particularly well on graphs with power law degree distribution, such as several real-world complex networks. Such rules provide a notable reduction in the size of the problem and, consequently, gains in scalability.

scholarly journals Width of a scale-free tree

2005 ◽  
Vol 42 (3) ◽  
pp. 839-850 ◽  
Author(s):  
Zsolt Katona
Keyword(s):  
Theoretical Result ◽  
Power Law ◽  
Real World ◽  
Degree Distribution ◽  
Graph Model ◽  
Random Graph Model ◽  
Scale Free ◽  
Slight Generalization ◽  
Free Tree ◽  
Law Degree

Consider the random graph model of Barabási and Albert, where we add a new vertex in every step and connect it to some old vertices with probabilities proportional to their degrees. If we connect it to only one of the old vertices then this will be a tree. These graphs have been shown to have a power-law degree distribution, the same as that observed in some large real-world networks. We are interested in the width of the tree and we show that it is at the nth step; this also holds for a slight generalization of the model with another constant. We then see how this theoretical result can be applied to directory trees.

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