The anatomy of a large-scale hypertextual Web search engine

Brin, Sergey; Page, Lawrence

doi:10.1016/s0169-7552(98)00110-x

Cited by 11,471 publications

(8,327 citation statements)

References 11 publications

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“…The pagerank of a node in a network of N nodes with adjacency matrix A ij can be calculated from where k out (j) is the number of outgoing links from node j and α is a reset parameter 1 . Equation (1) describes a diffusion process, where p t (i) is the frequency of visitation of node i by a particle at time step t that moves along the links of the network (encoded in the adjacency matrix A ij ) with probability α and jumps to a randomly chosen node with probability 1 − α.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the status of the recommending node is important-a letter of recommendation from a Nobel Laureate (that is, a node with high pagerank p t − 1 (j)) carries far more weight than 10 letters from academics of lesser prominence. However, if the Laureate has (1) (1) drafted a large number of recommendations for various candidates (has a high k out (j)), then his (her) status as a recommender drops.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Ranking stability and super-stable nodes in complex networks

2011

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Pagerank, a network-based diffusion algorithm, has emerged as the leading method to rank web content, ecological species and even scientists. Despite its wide use, it remains unknown how the structure of the network on which it operates affects its performance. Here we show that for random networks the ranking provided by pagerank is sensitive to perturbations in the network topology, making it unreliable for incomplete or noisy systems. In contrast, in scale-free networks we predict analytically the emergence of super-stable nodes whose ranking is exceptionally stable to perturbations. We calculate the dependence of the number of super-stable nodes on network characteristics and demonstrate their presence in real networks, in agreement with the analytical predictions. These results not only deepen our understanding of the interplay between network topology and dynamical processes but also have implications in all areas where ranking has a role, from science to marketing.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Ranking stability and super-stable nodes in complex networks

2011

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“…On the other hand, the tuple-based approaches aim at processing a keyword query by utilizing the weights associated with nodes (tuples) and edges (foreign-key reference) in a graph. Node/edge weights can be assigned using [7,4]. With the weighted graph, the tuple-based approaches find top-k minimum cost connections (trees) among tuples for a keyword query.…”

Section: (B)mentioning

confidence: 99%

Finding Top-k Min-Cost Connected Trees in Databases

Ding

Wang

et al. 2007

2007 IEEE 23rd International Conference on Data Engineering

272

274

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It is widely realized that the integration of database and information retrieval techniques will provide users with a wide range of high quality services. In this paper, we study processing an l-keyword query, p 1 , p 2 , · · · , p l , against a relational database which can be modeled as a weighted graph, G(V, E). Here V is a set of nodes (tuples) and E is a set of edges representing foreign key references between tuples. Let V i ⊆ V be a set of nodes that contain the keyword p i . We study finding top-k minimum cost connected trees that contain at least one node in every subset V i , and denote our problem as GST-k. When k = 1, it is known as a minimum cost group Steiner tree problem which is NPComplete. We observe that the number of keywords, l, is small, and propose a novel parameterized solution, with l as a parameter, to find the optimal GST-1, in time complexity O(3 l n + 2 l ((l + log n)n + m)), where n and m are the numbers of nodes and edges in graph G. Our solution can handle graphs with a large number of nodes. Our GST-1 solution can be easily extended to support GST-k, which outperforms the existing GST-k solutions over both weighted undirected/directed graphs. We conducted extensive experimental studies, and report our finding.

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“…As the prior probability that the Web page P is relevant to a query, p(P) is often computed via some link analysis method such as PageRank [34]. As the conditional probability of producing Q given P, p(Q|P) is computed using the anchor text of links to P [7], the text on P, and some retrieval model such as the language modeling approach [35].…”

Section: Ranking Web Pagesmentioning

confidence: 99%

Triggers and Monitoring in Intelligent Personal Health Record

Luo¹

2011

J Med Syst

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Although Web-based personal health records (PHRs) have been widely deployed, the existing ones have limited intelligence. Previously, we introduced expert system technology and Web search technology into the PHR domain and proposed the concept of an intelligent PHR (iPHR). iPHR provides personalized healthcare information to facilitate users' daily activities of living. The current iPHR is passive and follows the pull model of information distribution. This paper introduces triggers and monitoring into iPHR to make iPHR become active. Our idea is to let medical professionals pre-compile triggers and store them in iPHR's knowledge base. Each trigger corresponds to an abnormal event that may have potential medical impact. iPHR keeps collecting, processing, and analyzing the user's medical data from various sources such as wearable sensors. Whenever an abnormal event is detected from the user's medical data, the corresponding trigger fires and the related personalized healthcare information is pushed to the user using natural language generation technology, expert system technology, and Web search technology.

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The anatomy of a large-scale hypertextual Web search engine

Cited by 11,471 publications

References 11 publications

Ranking stability and super-stable nodes in complex networks

Ranking stability and super-stable nodes in complex networks

Finding Top-k Min-Cost Connected Trees in Databases

Triggers and Monitoring in Intelligent Personal Health Record

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