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2nd International Conference on Information Systems Design and Intelligent Applications (INDIA)

作者： Anand, Deepa Ravichandran, Sushma CMR Inst Technol Bangalore Karnataka India

ISBN: (纸本)9788132222477;9788132222460

Q&A forums offer users a platform to gain and showcase their knowledge and facilitate intellectual sharing among community members. An incentive based mechanism is devised as a means to encourage users to contribute to questions posed in the forum by allowing various users to Upvote and Downvote posts and answers. Though this provides the needed impetus to users to contribute quality content, oftentimes the reward mechanism is more biased towards questions on popular topics such as Java, html etc. Moreover simple questions-questions whose answer can be found in most textbooks or on the web-sometimes get a large number of votes due to a number of people finding the answer with the least amount of effort from their side. The downside of this is that quality questions-questions where the user has given thought to the topic, put in effort and which would require an effort from the answerer-is seldom answered or voted up. The investigations in this paper are oriented towards separating popularity from quality. We propose an iterative technique based on link analysis algorithms to separate the quality posts from the low quality ones even if the latter have a high number of upvotes. Such a scoring can be utilized for improving search and for highlighting good posts finding answers to which would enhance the knowledge level of the community as a whole. Experimental evaluations aligning the ranks produced with expert given ranks are promising. In two out of three cases the proposed approach had higher rank correlation with those of the experts as compared to ranks inferred through Stack Overflow assigned scores. Moreover examination of individual posts which refer to common questions illustrate that the proposed techniques have the ability to push down their scores even if Stack Overflow has assigned high scores.

关键词： Stack overflow Q&A forums pagerank algorithm Machine learning

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Identifying Main Topics in Density-based Spatial Clusters using Network-based Representative Document Extraction 8

Identifying Main Topics in Density-based Spatial Clusters us...

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8th IEEE International Workshop on Computational Intelligence and Applications (IWCIA)

作者： Sakai, Tatsuhiro Tamura, Keiichi Kitakami, Hajime Hiroshima City Univ Grad Sch Informat Sci Asa Minami Ku 3-4-1 Ozuka Higashi Hiroshima 7313194 Japan

ISBN: (纸本)9781479998869

Geo-tagged documents on social media are usually related to local topics and events. Extracting areas of interest associated with local "attractive" topics from geo-tagged documents is one of the most important challenges in many application domains. In this paper, we propose a novel method for extracting the areas of interest from geo-tagged documents. There are two main steps in the proposed method. First, the (epsilon, sigma)-density-based adaptive spatial clustering algorithm extracts areas where local topics are attracting attention as spatial clusters. Second, representative geo-tagged documents are detected to identify the main topic in each spatial cluster. The (epsilon, sigma)-density-based adaptive spatial clustering algorithm changes the threshold for seamlessly extracting spatial clusters regardless of the local densities of the posted geo-tagged documents. Moreover, the proposed method utilizes the network-based important sentence extraction method in order to extract representative geo-tagged documents from each spatial cluster. The experimental results show that the proposed method can extract the areas of interest as spatial clusters and representative documents as main topics.

关键词： Density-based spatial clustering pagerank algorithm Node-based betweenness centrality Geo-tagged tweet Local topic extraction

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Cointegration analysis and influence rank-A network approach to global stock markets

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2014年第0期400卷 168-185页

作者： Yang, Chunxia Chen, Yanhua Niu, Lei Li, Qian Nanjing Univ Informat Sci & Technol Nanjing 210044 Jiangsu Peoples R China

In this paper, cointegration relationships among 26 global stock market indices over the periods of sub-prime and European debt crisis and their influence rank are investigated by constructing and analyzing directed and weighted cointegration networks. The obtained results are shown as follows: the crises have changed cointegration relationships among stock market indices, their cointegration relationship increased after the Lehman Brothers collapse, while the degree of cointegration gradually decreased from the sub-prime to European debt crisis. The influence of US, Japan and China market indices are entirely distinguished over different periods. Before European debt crisis US stock market is a 'global factor' which leads the developed and emerging markets, while the influence of US stock market decreased evidently during the European debt crisis. Before sub-prime crisis, there is no significant evidence to show that other stock markets co-move with China stock market, while it becomes more integrated with other markets during the sub-prime and European debt crisis. Among developed and emerging stock markets, the developed stock markets lead the world stock markets before European debt crisis, while due to the shock of sub-prime and European debt crisis, their influences decreased and emerging stock markets replaced them to lead global stock markets. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/3.0/).

关键词： Stock markets Cointegration Complex network pagerank algorithm

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Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation

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BMC MEDICAL GENOMICS 2014年第2-Sup期7卷 S4-S4页

作者： Li, Min Zhang, Jiayi Liu, Qing Wang, Jianxin Wu, Fang-Xiang Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Univ Saskatchewan Engn Coll Saskatoon SK Canada

Background: Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN). Results: Considering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A pagerank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the pagerank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia. Conclusions: Tissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The pagerank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.

关键词： Protein Interaction Network pagerank algorithm Human Protein Interaction Network Weighted Degree Centrality pagerank Method

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Pinning Scheme for Complex Networks Based on pagerank algorithm

Pinning Scheme for Complex Networks Based on PageRank Algori...

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2010 International Conference on Intelligent Computing and Integrated Systems

作者： Daniel W.C.Ho Department of Mathematics City University of Hong Kong

The pinning control problem of complex networks has been widely investigated recent ***,there does not exist a widely accepted pinning scheme which can be applied to the synchronization of complex *** contribution gives a novel pinning scheme which based on the famous pagerank algorithm to make the network realize synchronization as fast as *** simulation indicates that this pinning scheme is effective for undirected networks,however,it fails when it applied to directed networks.

关键词： Complex networks pinning scheme undirected networks directed networks ER random graph scalefree networks pagerank algorithm

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Inferring influenza global transmission networks without complete phylogenetic information

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EVOLUTIONARY APPLICATIONS 2014年第3期7卷 403-412页

作者： Aris-Brosou, Stephane Univ Ottawa Dept Biol Ctr Adv Res Environm Genom Ottawa ON K1N 6N5 Canada Univ Ottawa Dept Math & Stat Ottawa ON K1N 6N5 Canada

Influenza is one of the most severe respiratory infections affecting humans throughout the world, yet the dynamics of its global transmission network are still contentious. Here, I describe a novel combination of phylogenetics, time series, and graph theory to analyze 14.25years of data stratified in space and in time, focusing on the main target of the human immune response, the hemagglutinin gene. While bypassing the complete phylogenetic inference of huge data sets, the method still extracts information suggesting that waves of genetic or of nucleotide diversity circulate continuously around the globe for subtypes that undergo sustained transmission over several seasons, such as H3N2 and pandemic H1N1/09, while diversity of prepandemic H1N1 viruses had until 2009 a noncontinuous transmission pattern consistent with a source/sink model. Irrespective of the shift in the structure of H1N1 diversity circulation with the emergence of the pandemic H1N1/09 strain, US prevalence peaks during the winter months when genetic diversity is at its lowest. This suggests that a dominant strain is generally responsible for epidemics and that monitoring genetic and/or nucleotide diversity in real time could provide public health agencies with an indirect estimate of prevalence.

关键词： genetic diversity global dynamics influenza pagerank algorithm prevalence transmission network

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The p-index: Ranking Scientists using Network Dynamics

The p-index: Ranking Scientists using Network Dynamics

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14th Annual International Conference on Computational Science

作者： Senanayake, U. Piraveenan, M. Zomaya, A. Y. Univ Sydney Ctr Complex Syst Res Sydney NSW 2006 Australia Univ Sydney Ctr Distributed & High Performance Comp Sydney NSW Australia

The indices currently used by scholarly databases, such as Google scholar, to rank scientists, do not attach weights to the citations. Neither is the underlying network structure of citations considered in computing these metrics. This results in scientists cited by well-recognized journals not being rewarded, and may lead to potential misuse if documents are created purely to cite others. In this paper we introduce a new ranking metric, the p-index (pagerank-index), which is computed from the underlying citation network of papers, and uses the pagerank algorithm in its computation. The index is a percentile score, and can potentially be implemented in public databases such as Google scholar, and can be applied at many levels of abstraction. We demonstrate that the metric aids in fairer ranking of scientists compared to h-index and its variants. We do this by simulating a realistic model of the evolution of citation and collaboration networks in a particular field, and comparing h-index and p-index of scientists under a number of scenarios. Our results show that the p-index is immune to author behaviors that can result in artificially bloated h-index values.

关键词： p-index h-index ranking scientists complex networks pagerank algorithm complex systems scholarly databases Google scholar

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Construction and Influence Analysis of Co-author Network based on Network Science

Construction and Influence Analysis of Co-author Network bas...

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International Conference on Advanced Nano-Technology and Biomedical Material (ANTBM)

作者： Yang, You-she Zheng, Ming-fa He, Qi-fang Wang, Tie-zhu Air Force Engn Univ Coll Sci Xian 710051 Peoples R China Coll Xijing Dept Math Xian 710236 Peoples R China Air Force Engn Univ Informat & Nav Coll Xian 710077 Peoples R China Air Force Engn Univ Informat Ctr Xian 710051 Peoples R China

ISBN: (纸本)9783038352044

In this paper, we are devoted to the construction and analysis of research network. Firstly, on the basis of data Erdos1, a co-author network is built and two novel measures are proposed to analyze properties of the co-author network. A data extraction method using string matching technique is developed and the network is visualized using UCINET. Then, the first-order and second-order entropy are defined to depict the complexity of the network, and the node's invalidity probability and load-bearing capacity are defined to depict the robustness of the network.

关键词： Network construction Co-author network Importance degree pagerank algorithm

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Ranking the Results of DBpedia Retrieval with SPARQL Query

Ranking the Results of DBpedia Retrieval with SPARQL Query

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3rd Joint International Conference on Semantic Technology

作者： Ichinose, Shiori Kobayashi, Ichiro Iwazume, Michiaki Tanaka, Kouji Ochanomizu Univ Grad Sch Humanities & Sci Tokyo 112 Japan Univ Commun Res Inst NICT Kyoto Japan

ISBN: (纸本)9783319068268;9783319068251

In recent years, a number of Semantic Web databases have been actively open to public as common resources on the Web by the effort of Linked Open Data community project. Due to this, we need a good method to search necessary data from those databases, depending on various purposes. In this study, we propose two methods to rank the results retrieved by a SPARQL query (especially, a SELECT query), using the information about the frequency of each property in a data set and the links between RDF resources. In order to evaluate our proposed methods, we set two cases for using SPARQL queries, and then rank the query results in each case. The usefulness of our proposed method has been confirmed by subject experiments.

关键词： DBpedia SPARQL Ranking pagerank algorithm Semantic Web

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Construction and Influence Analysis of Co-author Network based on Network Science

Construction and Influence Analysis of Co-author Network bas...

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2014 International Conference on Advanced Nano-Technology and Biomedical Material(ANTBM 2014)

作者： You-she Yang Ming-fa Zheng Qi-fang He cand Tie-zhu Wang Department of Mathematics College of Xijing Science College Air Force Engineering University Information and Navigation College Air Force Engineering University Information Center Air Force Engineering University

关键词： Network construction Co-author network Importance degree pagerank algorithm

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