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11th International Conference on Enterprise Information Systems

作者： Abdo, Mahmoud Zaki Ismail, Manal Ahmed Eladawy, Mohamed Ibraheem Helwan Univ Dept Commun Fac Engn Cairo Egypt

ISBN: (纸本)9789898111852

The amount of global information in the World Wide Web is growing at an incredible rate. Millions of results are returned from search engines. The rank of pages in the search engines is very important. One of the basic rank algorithms is pagerank algorithm. This paper proposes an enhancement of pagerank algorithm to speed up the computational process. The enhancement of pagerank algorithm depends on using the Ant algorithm. On average, this technique yields about 7.5 out of ten relevant pages to the query topic, and the total time reduced by 19.9 %.

关键词： Search engine pagerank algorithm Ranking algorithms Ant algorithm

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A Web Aggregation Approach for Distributed Randomized pagerank algorithms

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2012年第11期57卷 2703-2717页

作者： Ishii, Hideaki Tempo, Roberto Bai, Er-Wei Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Yokohama Kanagawa 2268502 Japan Politecn Torino CNR IEIIT I-10129 Turin Italy Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland

The pagerank algorithm employed at Google assigns a measure of importance to each web page for rankings in search results. In our recent papers, we have proposed a distributed randomized approach for this algorithm, where web pages are treated as agents computing their own pagerank by communicating with linked pages. This paper builds upon this approach to reduce the computation and communication loads for the algorithms. In particular, we develop a method to systematically aggregate the web pages into groups by exploiting the sparsity inherent in the web. For each group, an aggregated pagerank value is computed, which can then be distributed among the group members. We provide a distributed update scheme for the aggregated pagerank along with an analysis on its convergence properties. The method is especially motivated by results on singular perturbation techniques for large-scale Markov chains and multi-agent consensus. A numerical example is provided to illustrate the level of reduction in computation while keeping the error in rankings small.

关键词： Aggregation distributed computation multi-agent consensus pagerank algorithm randomization search engines stochastic matrices

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Detection of overlapping protein complexes using a protein ranking algorithm 9th

Detection of overlapping protein complexes using a protein r...

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9th Annual International Conference on Innovations in Information Technology (IIT)

作者： Hanna, Eileen Marie United Arab Emirates Univ Coll Informat Technol Al Ain U Arab Emirates

ISBN: (纸本)9781467362030

The detection of protein complexes is evidently a cornerstone of understanding various biological processes and identifying key genes causing different diseases. Accordingly, many methods aiming at detecting protein complexes were developed. Recently, a novel method called ProRank was introduced. This method uses a ranking algorithm to detect protein complexes by ordering proteins based on their importance in the interaction network and by accounting for the evolutionary relationships among them. The experimental results showed that ProRank outperformed several well-known methods in terms of the number of detected complexes with high accuracy, precision and recall levels. In this paper, we overcome a drawback of the ProRank algorithm and further improve its performance by allowing detected protein complexes to overlap;a supposition that was not considered in the original version of the method.

关键词： overlapping protein complexes protein-protein interaction pagerank algorithm essential protein

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Evolution Analysis of Online Knowledge Transfer Network

Evolution Analysis of Online Knowledge Transfer Network

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4th International Conference on Information Computing and Applications (ICICA)

作者： Wei, Jing Song, Ruixiao Miao, Jianjun Xu, Zan Nanjing Univ Post & Telecommun Coll Econ & Management Nanjing 210046 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Peoples R China

ISBN: (纸本)9783642539329;9783642539312

Based on the study of online knowledge transfer network`s topology and dynamic statistical characteristics, this paper found the characteristic differences between the evolution of online knowledge transfer network and the traditional BA network model. Through the empirical data analysis of a BBS forum, it gives out an evolution model of the online knowledge transfer network based on the pagerank algorithm. Meanwhile, in the network analysis it also found that the network growth model has a power-law distribution of degree. Through the control of the attenuation coefficient and the node degree growth factor, it can ultimately realize growth control of the online knowledge transfer network.

关键词： knowledge management knowledge transfer complex network BBS pagerank algorithm

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pagerank-based Traffic Simulation Using Taxi Probe Data

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Procedia Computer Science 2013年 22卷 1156-1163页

作者： Naoto Mukai Department of Culture-Information Studies School of Culture-Information Studies Sugiyama Jogakuen University 17-3 Hoshigaoka-motomachi Chikusa-ku Nagoya Aichi 464-8662 Japan

Traffic analysis is an important work to transport people and goods to their destinations in a quick and efficient manner. In these years, probe taxis, which equip with some sensors (e.g., global positioning system), become widely used in Tokyo. Additionally, their sensor data enable us to predict the amount of future traffic. In this paper, we report a web-based viewer of taxi probe data which are used for traffic simulation. Moreover, we propose a traffic simulation model based on the concept of “pagerank”, which is used for the Google search engine, to gain a quick overview of traffic flow in Tokyo. The original algorithm of “pagerank” needs a matrix calculation called “Google matrix”, but the matrix calculation can be simulated by transitions of web surfers among web pages. Thus, we also assume the transitions of taxis as the same characteristics of the web surfers, and develop a traffic simulator to find important spots which have a strong effect on transportation efficiency.

关键词： Traffic Simulation Model Taxi Probe Data pagerank algorithm

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Key Nodes Mining in Transport Networks Based on pagerank algorithm

Key Nodes Mining in Transport Networks Based on PageRank Alg...

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2009中国控制与决策会议

作者： Gao Lianxiong~(1,2),Wu Jianping,Liu Rui~3 1.School of Computer,Beijing University of Posts & Telecommunications,Beijing 100876 2.School of Physics & Electronics,Yunnan Nationalities University,Kunming 650031,China 3.School of Resources-environment and Geoscience,Yunnan University,Kunming 650091,China

Transport networks display the features of complex networks,in which the vertices importance measurement is *** analyzing some classic importance measurements and the characteristics of transport networks, NodeRank,a new method based on pagerank algorithm,is proposed in this paper to measure the importance of vertices in transportation *** the constraint equation is deduced and the existence and uniqueness of solutions are *** solving algorithm is described and its convergence is ***,we present a case applying our method to mining key nodes in a real-world transport network.

关键词： Complex Network pagerank algorithm Transport Network Key Nodes Mining

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pagerank computation via a distributed randomized approach with lossy communication

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SYSTEMS & CONTROL LETTERS 2012年第12期61卷 1221-1228页

作者： Ishii, Hideaki Tempo, Roberto Bai, Er-Wei Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku Yokohama Kanagawa 2268502 Japan Politecn Torino CNR IEIIT I-10129 Turin Italy Univ Iowa Dept Elect & Comp Engn Seamans Ctr Engn Arts & Sci 4316 Iowa City IA 52242 USA Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland

For ranking web pages in search results, Google employs the so-called pagerank algorithm, which provides a measure of importance to each page based on the web structure. Recently, we have developed a distributed randomized approach for this algorithm, where pages compute their own pagerank by communicating over selected links. In this paper, the focus is on the effects of unreliability in communication channels. Specifically, we consider random data losses modeled as a Markov chain and introduce a generalized version of the distributed scheme. Its convergence properties and the error in the approximated pagerank are analyzed. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Distributed computation Multi-agent consensus pagerank algorithm Randomization Search engines Stochastic matrices

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An application of Google’s pagerank to NFL rankings

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Involve 2012年第4期5卷 463-471页

作者： Zack, Laurie Lamb, Ron Ball, Sarah High Point University 833 Montlieu Avenue High Point 27262 NC United States

We explain the pagerank algorithm and its application to the ranking of football teams via the GEM method. We then modify and extend the GEM method with the addition of more football statistics to look at the possibility of using this method to more accurately rank teams. Lastly, we compare both methods by aggregating each statistical ranking using the cross-entropy Monte Carlo algorithm. © 2012 Mathematical Sciences Publishers.

关键词： linear algebra pagerank algorithm ranking football teams

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Identifying the greatest team and captain-A complex network approach to cricket matches

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2012年第23期391卷 6066-6076页

作者： Mukherjee, Satyam Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA

We consider all Test matches played between 1877 and 2010 and One Day International (ODI) matches played between 1971 and 2010. We form directed and weighted networks of teams and also of their captains. The success of a team (or captain) is determined by the 'quality' of the wins, not simply by the number of wins. We apply the diffusion-based pagerank algorithm to the networks to assess the importance of the wins, and rank the respective teams and captains. Our analysis identifies Australia as the best team in both forms of cricket, Test and ODI. Steve Waugh is identified as the best captain in Test cricket and Ricky Panting is the best captain in the ODI format. We also compare our ranking scheme with an existing ranking scheme, the Reliance ICC ranking. Our method does not depend on 'external' criteria in the ranking of teams (captains). The purpose of this paper is to introduce a revised ranking of cricket teams and to quantify the success of the captains. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Social network analysis Cricket pagerank algorithm

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Detection of protein complexes using a protein ranking algorithm

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2012年第10期80卷 2459-2468页

作者： Zaki, Nazar Berengueres, Jose Efimov, Dmitry UAEU Fac Informat Technol Al Ain U Arab Emirates Moscow MV Lomonosov State Univ Fac Mech & Math Moscow Russia

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. There is ample evidence that many disease mechanisms involve protein complexes, and being able to predict these complexes is important to the characterization of the relevant disease for diagnostic and treatment purposes. This article introduces a novel method for detecting protein complexes from PPI by using a protein ranking algorithm (ProRank). ProRank quantifies the importance of each protein based on the interaction structure and the evolutionarily relationships between proteins in the network. A novel way of identifying essential proteins which are known for their critical role in mediating cellular processes and constructing protein complexes is proposed and analyzed. We evaluate the performance of ProRank using two PPI networks on two reference sets of protein complexes created from Munich Information Center for Protein Sequence, containing 81 and 162 known complexes, respectively. We compare the performance of ProRank to some of the well known protein complex prediction methods (ClusterONE, CMC, CFinder, MCL, MCode and Core) in terms of precision and recall. We show that ProRank predicts more complexes correctly at a competitive level of precision and recall. The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of the proposed method. Proteins 2012;. (C) 2012 Wiley Periodicals, Inc.

关键词： pagerank algorithm protein-protein interaction essential protein evolutionarily relationships pairwise alignment

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