PageRank Method Research Articles

Identifying patterns and structural influences in the scientific communication of business knowledge

This study uses several quantitative techniques to enable a multidimensional analysis of 47 key business journals by analyzing the scientific communication patterns and structural influences of these journals. Apart from using clustering techniques to establish research clusters in the Business domain, we apply a refined PageRank method by differentiating between the citation types to enable a cross-sectional evaluation of the selected journals. The results indicate that the five most influential journals are from Finance and Economics. The selected Finance journals are knowledge hubs and the selected Economics journals are knowledge sources when ISI's entire journal database is considered. However, within the Business domain, the selected Finance journals appear to be high impact knowledge hubs while the selected Economics journals appear to be high impact journals despite weak citation activity. All in all, such analyses are beneficial to scholars when selecting publication outlets to showcase their research, and to agencies such Financial Times and Bloomberg when selecting their journals basket for their annual journal evaluation exercises.

Excavation of critical resource node for quality control of multi-variety mixed production shopfloor based on complex network property

The complexity of multi-variety mixed production mode makes it difficult to find out the critical points to guide the quality control and improvement activities of the shopfloor. To excavate these critical nodes, a complex network analyze method is proposed in this article. A quality control network of multi-variety mixed production is constructed based on directed weighted network using unqualified batches’ manufacturing information. In order to evaluate the critical degree of a manufacturing resource node of quality control network, this article proposes a QCD-NodeRank indicator and an evaluation method based on improvement of PageRank method. This critical degree evaluation method reflects the connection relationship of manufacturing resource nodes and considers the quality situation of them. The manufacturing resource node with high QCD-NodeRank values is considered to be the critical one. Then, a hierarchical clustering method is introduced to evaluate the critical nodes for quality control. Finally, an example is provided to illustrate the proposed approach.

Analysis of User Influence Using User Behavior and Random Walk

To improve the effect of user influence predicting in microblog, this paper proposed a new method of user influence analysis named UBRWR, based on user interactive behavior and the random walk method. UBRWR firstly used the behaviors between users to reconstruct the network topology, and then an improved PageRank algorithm was applied to predict the user influence, with quantized individual attribute features. The experimental in Weiba show that the UBRWR algorithm outperforms the PageRank algorithm and method using fans count in terms of ranking accuracy.

OR/MS journals evaluation based on a refined PageRank method: an updated and more comprehensive review

The purpose of this research is to furnish the OR/MS research community with an updated assessment of the discipline's journals set with refinements that also highlight the various characteristics of OR/MS journals. More specifically, we apply a refined PageRank method initially proposed by Xu et al. (2011) to evaluate the top 31 OR/MS journals for 2010, and report our findings. We also report the shifts in the rankings that span 5 years, from 2006 to 2010. We observe that Manufacturing and Service Operations Management, indexed by the SCI only in 2008, is a specialized journal that is consistently highly regarded within the discipline. The rankings also suggest that Management Science is more established as a generalized journal as it has more external impact. In general, our ranking results correlate with expert opinions, and we also observe, report and discuss some interesting patterns that have emerged over the past 5 years from 2006 to 2010.

Measuring the spreadability of users in microblogs

Message forwarding (e.g., retweeting on Twitter.com) is one of the most popular functions in many existing microblogs, and a large number of users participate in the propagation of information, for any given messages. While this large number can generate notable diversity and not all users have the same ability to diffuse the messages, this also makes it challenging to find the true users with higher spreadability, those generally rated as interesting and authoritative to diffuse the messages. In this paper, a novel method called SpreadRank is proposed to measure the spreadability of users in microblogs, considering both the time interval of retweets and the location of users in information cascades. Experiments were conducted on a real dataset from Twitter containing about 0.26 million users and 10 million tweets, and the results showed that our method is consistently better than the PageRank method with the network of retweets and the method of retweetNum which measures the spreadability according to the number of retweets. Moreover, we find that a user with more tweets or followers does not always have stronger spreadability in microblogs.

NewFriends: an algorithm for computing the minimum number of friends required by a user to get the highest PageRank in a social network

The online social networking phenomenon is growing rapidly all around the world. As a consequence, in recent years, several studies have been devoted to the analysis of social network sites. A specific issue that has been addressed is the identification of leaders of the social network based on well-known algorithms such as PageRank. In this paper, we propose a novel algorithm based on the PageRank method to determine the minimum number of new friends required by a user of a social network to become the user with the highest PageRank in the network. We provide formal mathematical definitions and validate our proposal with some experiments based on artificial and real data.

Predicting the Next Accessed Web Page Using Markov Model and PageRank

In this highly competitive world, only a user can decide the future of a web-based business. Success of any web business directly depends on services provided to the users and the level of satisfaction experienced by users. Prediction algorithm can be a dominating factor in web businesses that includes offers to customers on selling items, web site personalization and web recommendations etc. Understanding user's behaviour through Markov model can be a simple approach, but ambiguous prediction of next accessed web page is the main drawback of this approach. Therefore a method is proposed in this paper, in which ambiguous prediction problem can be resolved using Web PageRank along with Markov Model. Experimental result shows reduced number of ambiguous predictions after applying PageRank method.

Do Links Matter? An Investigation of the Impact of Consumer Feedback, Recommendation Networks, and Price Bundling on Sales

Link structures among items within an E-commerce Web site can be regarded as a potential recommendation that helps new consumers quickly locate relevant products. In this paper, combining a modified version of Google's PageRank method with economic analysis of word of mouth, we investigate whether a product's position within a network composed of recommendation links is incrementally informative about its future sales. Based on data from Amazon.com, we document that with consumer word of mouth and other product characteristics controlled, the position of a product within a recommendation network does influence consumers' purchase decisions, and models incorporating link structure have a higher incremental predictive power of future sales than models without. In addition, as time elapses, the relative weights consumers placed on recommendations with price discount and those without are different. Last, we develop a learning mechanism through which we find the optimal damping value of the PageRank IR model in the Amazon context. Our results show that compared to general Internet surfing behavior, consumer consumption on Amazon is less random. We conclude that even though the product position within a recommendation network does influence customers' purchase behaviors, product sales are still mainly driven by their own product characteristics.

An Improved PageRank Method based on Genetic Algorithm for Web Search

Web search engine has become a very important tool for finding information efficiently from the massive Web data. Based on PageRank algorithm, a genetic PageRank algorithm (GPRA) is proposed. With the condition of preserving PageRank algorithm advantages, GPRA takes advantage of genetic algorithm so as to solve web search. Experimental results have shown that GPRA is superior to PageRank algorithm and genetic algorithm on performance.

PENINGKATAN EFEKTIVITAS PENYAJIAN SEARCH RESULT DARI SISTEM TEMU KEMBALI INFORMASI MENGGUNAKAN CLUSTERING DOKUMEN

The fast expansion of text information volume has caused the difficulty of infor-mation retrieval process, mainly on the model of word-based matching. The synonymy factor of word has caused non relevant document to be retrieved, whereas the polisemy factor has caused relevant document not to be retrieved. The application of document clustering to the search results before presented to the user can increase the effect-tiveness of retrieval. This study elaborates the application of document clustering to im-prove the effectiveness of retrieval by clustering to the search result before presented to the user. Three clustering algorithms from partitional approach i.e. K-Means, Bisecting K-Mean and Buckshot, and hierarchical agglomerative approach with two cluster similarity function i.e. UPGMA and Complete Link were chosen. The performance parameter was measured using F-measure, a metric derived from Precision and Recall of retrieval. The document collections to be tested are 1000 news document and 350 academic abstract documents. The results show that the presentation of search results by using clustering has improved the number of relevant document in the up-level ranks. The improvement was statistically significant compare to the page-rank method. The improvement of F-measure as a performance metric is about 14,34% for news documents and 28,18% for abstract documents.

Global alignment of multiple protein interaction networks with application to functional orthology detection

Protein-protein interactions (PPIs) and their networks play a central role in all biological processes. Akin to the complete sequencing of genomes and their comparative analysis, complete descriptions of interactomes and their comparative analysis is fundamental to a deeper understanding of biological processes. A first step in such an analysis is to align two or more PPI networks. Here, we introduce an algorithm, IsoRank, for global alignment of multiple PPI networks. The guiding intuition here is that a protein in one PPI network is a good match for a protein in another network if their respective sequences and neighborhood topologies are a good match. We encode this intuition as an eigenvalue problem in a manner analogous to Google's PageRank method. Using IsoRank, we compute a global alignment of the Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, and Homo sapiens PPI networks. We demonstrate that incorporating PPI data in ortholog prediction results in improvements over existing sequence-only approaches and over predictions from local alignments of the yeast and fly networks. Previous methods have been effective at identifying conserved, localized network patterns across pairs of networks. This work takes the further step of performing a global alignment of multiple PPI networks. It simultaneously uses sequence similarity and network data and, unlike previous approaches, explicitly models the tradeoff inherent in combining them. We expect IsoRank-with its simultaneous handling of node similarity and network similarity-to be applicable across many scientific domains.

Ranking USPTO Patent Documents by Importance Using Random Surfer Method (PageRank)

This study attempted to construct an alternative method for ranking patents by their importance in order to improve the existing techniques of determining citation weighted patent stocks. We apply the random surfer PageRank method to the problem of ranking the importance of the patent documents issued to inventors by the United States Patent and Trademark Office. PageRank patent importance weights turn out to differ systematically from the weights based on the number of backward or forward citations. Yet PageRanks perform similarly to the knowledge transfer indicator, which is the ratio of forward to backward citations.

The Juxtaposed approximate PageRank method for robust PageRank approximation in a peer-to-peer web search network

We present Juxtaposed approximate PageRank (JXP), a distributed algorithm for computing PageRank-style authority scores of Web pages on a peer-to-peer (P2P) network. Unlike previous algorithms, JXP allows peers to have overlapping content and requires no a priori knowledge of other peers' content. Our algorithm combines locally computed authority scores with information obtained from other peers by means of random meetings among the peers in the network. This computation is based on a Markov-chain state-lumping technique, and iteratively approximates global authority scores. The algorithm scales with the number of peers in the network and we show that the JXP scores converge to the true PageRank scores that one would obtain with a centralized algorithm. Finally, we show how to deal with misbehaving peers by extending JXP with a reputation model.

Education

When you think of searching for information on the Web, you probably think of search engines such as Google. But do you think of eigenvectors? You should! Faithful readers of the Education section may recall the connection between information retrieval and numerical linear algebra from the second issue of the Education section, in 1999. In the article "Matrices, Vector Spaces, and Information Retrieval" (volume 41, pp. 335--362), Berry, Drmac, and Jessup described the use of latent semantic analysis, which is based upon the singular value decomposition, for searching document collections. The paper in this issue's Education section, "A Survey of Eigenvector Methods for Web Information Retrieval," extends this topic to the modern world of the Web. As the authors, Amy Langville and Carl Meyer, point out, information retrieval on the Web is significantly more challenging and difficult than information retrieval in a collection of documents, due to the enormous scale of the information space, the critical role of the hyperlink structure of the Web, and the greater fluidity of the space. Their paper summarizes the main approaches that have been taken to ranking Web pages in response to queries in search engines. It shows readily that the two basic approaches both lead to an eigenvector problem that is naturally solved by the power method. In the case of the PageRank method used by Google, this problem can have several billion variables! Fortunately, it does not have to be solved very often. The paper provides examples of the use of many concepts in matrix algebra, including stochastic and irreducible matrices. It also points to various promising areas for additional research. As such, it will be useful both in introductory numerical analysis and numerical linear algebra courses, where its very pleasant style and real-world context should make it popular with students, and in more advanced courses where it will provide a foundation for deeper study. It is fitting that as this paper on information retrieval brings the Education section full circle after six years, leadership of the section is changing hands. By the time this issue is published, the Section Editor role will be in the very capable hands of Professor Andrew Bernoff of Harvey Mudd College. It has been a true pleasure serving as editor of this section, due to the wonderful support of the SIAM staff (thanks particularly to Brian Fauth, Lou Primus, Mitch Chernoff, and Mary Rose Muccie), to a superb editorial board, and, most of all, to the many authors who have contributed their excellent work to SIAM Review. Thanks so much to all of you!

PageRank and Interaction Information Retrieval

AbstractThe PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady‐state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (I2R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based on dynamic systems. In the present paper, a different interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.