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PageRank is a way of measuring the importance of website pages. According to Google: PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites. [1]
The pagerank is a highly unstable measure, showing frequent rank reversals after small adjustments of the jump parameter. [18] While the failure of centrality indices to generalize to the rest of the network may at first seem counter-intuitive, it follows directly from the above definitions. Complex networks have heterogeneous topology.
In graph theory, eigenvector centrality (also called eigencentrality or prestige score [1]) is a measure of the influence of a node in a connected network.Relative scores are assigned to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes.
A simple social network: the nodes represent people or actors and the edges between nodes represent some relationship between actors. Katz centrality computes the relative influence of a node within a network by measuring the number of the immediate neighbors (first degree nodes) and also all other nodes in the network that connect to the node under consideration through these immediate neighbors.
For example, PageRank or document's length. Such features can be precomputed in off-line mode during indexing. Such features can be precomputed in off-line mode during indexing. They may be used to compute document's static quality score (or static rank ), which is often used to speed up search query evaluation.
SimRank is a general similarity measure, based on a simple and intuitive graph-theoretic model.SimRank is applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects.
The computational cost of the algorithm is a crucial factor since HITS and SALSA are computed at query time and can therefore significantly affect the response time of a search engine. This should be contrasted with query-independent algorithms like PageRank that can be computed off-line.
These two formulations of DCG are the same when the relevance values of documents are binary; [3]: 320 {,}. Note that Croft et al. (2010) and Burges et al. (2005) present the second DCG with a log of base e, while both versions of DCG above use a log of base 2.