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PAGERANK COMPUTATION, WITH SPECIAL ATTENTION TO DANGLING NODES
"... Abstract. We present a simple algorithm for computing the PageRank (stationary distribution) of the stochastic Google matrix G. The algorithm lumps all dangling nodes into a single node. We express lumping as a similarity transformation of G, and show that the PageRank of the nondangling nodes can b ..."
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Abstract. We present a simple algorithm for computing the PageRank (stationary distribution) of the stochastic Google matrix G. The algorithm lumps all dangling nodes into a single node. We express lumping as a similarity transformation of G, and show that the PageRank of the nondangling nodes can be computed separately from that of the dangling nodes. The algorithm applies the power method only to the smaller lumped matrix, but the convergence rate is the same as that of the power method applied to the full matrix G. The efficiency of the algorithm increases as the number of dangling nodes increases. We also extend the expression for PageRank and the algorithm to more general Google matrices that have several different dangling node vectors, when it is required to distinguish among different classes of dangling nodes. We also analyze the effect of the dangling node vector on the PageRank, and show that the PageRank of the dangling nodes depends strongly on that of the nondangling nodes but not vice versa. At last we present a Jordan decomposition of the Google matrix for the (theoretical) extreme case when all web pages are dangling nodes.
When rank trumps precision: Using the power method to compute Google’s PageRank.
, 2007
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“Accelerating Google PageRank by applying Aitken Extrapolation and Quadratic Extrapolation“
, 2015
"... Dr.ir. M.B. van Gijzen Other members of the graduation committee Prof.dr.ir. C. Vuik Dr. J.L.A. Dubbeldam Dr. B. van den Dries ..."
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Dr.ir. M.B. van Gijzen Other members of the graduation committee Prof.dr.ir. C. Vuik Dr. J.L.A. Dubbeldam Dr. B. van den Dries
Profile Based Search Engine
"... AbstractHuge amount of information available on internet makes it difficult for the user to get the exact search results according to his preferences. In this paper, we attempt to solve this problem to certain extent by extending the NUTCH open source search engine using personalized information o ..."
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AbstractHuge amount of information available on internet makes it difficult for the user to get the exact search results according to his preferences. In this paper, we attempt to solve this problem to certain extent by extending the NUTCH open source search engine using personalized information of user. The user's information will be extracted from the social networking sites like Facebook. The search keywords given by user will be input to the NUTCH search engine. The results returned by NUTCH search engine will be further refined using our own Profile Biasing Algorithm.
Linguistics 336: Words, networks, and complex systems
"... • Prerequisites: The course is intended for students who are interested understanding language as a system, and for students who are interested in using computational linguistic methods to study human social and cognitive dynamics. The prerequisities are at least one of: LING 3340 20 Introduction t ..."
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• Prerequisites: The course is intended for students who are interested understanding language as a system, and for students who are interested in using computational linguistic methods to study human social and cognitive dynamics. The prerequisities are at least one of: LING 3340 20 Introduction to Computational Linguistics, LING 361 Morphology, or LING 3300 Research Methods in Linguisticsm or equivalent background in computational linguistics, statistical natural language processing, and/or theories of lexical representation and processing. • Objectives: learn to
An evaluation of SimRank and Personalized PageRank to build a recommender system for the Web of Data
"... The Web of Data is the natural evolution of the World Wide Web from a set of interlinked documents to a set of interlinked entities. It is a graph of information resources interconnected by semantic relations, thereby yielding the name Linked Data. The proliferation of Linked Data is for sure an o ..."
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The Web of Data is the natural evolution of the World Wide Web from a set of interlinked documents to a set of interlinked entities. It is a graph of information resources interconnected by semantic relations, thereby yielding the name Linked Data. The proliferation of Linked Data is for sure an opportunity to create a new family of dataintensive applications such as recommender systems. In particular, since contentbased recommender systems base on the notion of similarity between items, the selection of the right graphbased similarity metric is of paramount importance to build an effective recommendation engine. In this paper, we review two existing metrics, SimRank and PageRank, and investigate their suitability and performance for computing similarity between resources in RDF graphs and investigate their usage to feed a contentbased recommender system. Finally, we conduct experimental evaluations on a dataset for musical artists and bands recommendations thus comparing our results with two other contentbased baselines measuring their performance with precision and recall, catalog coverage, items distribution and novelty metrics.
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"... Our background knowledge significantly influences the ways in which we perceive, categorize, reason, and make decisions in the world (Murphy, 2002). Recently, a number of researchers have argued that it is the causal element in our background knowledge that makes it particularly useful to us (Anders ..."
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Our background knowledge significantly influences the ways in which we perceive, categorize, reason, and make decisions in the world (Murphy, 2002). Recently, a number of researchers have argued that it is the causal element in our background knowledge that makes it particularly useful to us (Anderson & Lindsay, 1998; Keil, 2006). Causal and explanatory background knowledge can guide our causal