Number Of Incoming Links Research Articles

A Multi-Criteria Ranking Algorithm Based on the VIKOR Method for Meta-Search Engines

Ranking of web pages is one of the most important parts of search engines and is, in fact, a process that through it the quality of a page is estimated by the search engine. In this study, a ranking algorithm based on VIKOR multi-criteria decision-making method for Meta-Search Engines (MSEs) was proposed. In this research, the considered MSE first will receive the suggested pages associated with the search term from eight search engines including, Teoma, Google, Yahoo!, AlltheWeb, AltaVista, Wisenut, ODP, MSN. The results, at most 10 first pages are selected from each search engine and creates the initial dataset contains 80 web pages. The proposed parser is then executed on these pages and the eight criteria including the rank of web page in the related search engine, access time, number of repetitions of search terms, positions of search term at the webpage, numbers of media at the webpage, the number of imports in the webpage, the number of incoming links, and the number of outgoing links are extracted from these web pages. Finally, by using the VIKOR method and these extracted criteria, web pages will rank and 10 top results will be provided for the user. To implement the proposed method, JAVA and MATLAB languages are used. In the experiments, the proposed method is implemented for a query and its ranking results have been compared in terms of accuracy with three famous search engine including Google, Yahoo, and MSN. The results of comparisons show that the proposed method offers higher accuracy.

The distorted mirror of Wikipedia: a quantitative analysis of Wikipedia coverage of academics

Activity of modern scholarship creates online footprints galore. Along with traditional metrics of research quality, such as citation counts, online images of researchers and institutions increasingly matter in evaluating academic impact, decisions about grant allocation, and promotion. We examined 400 biographical Wikipedia articles on academics from four scientific fields to test if being featured in the world's largest online encyclopedia is correlated with higher academic notability (assessed through citation counts). We found no statistically significant correlation between Wikipedia articles metrics (length, number of edits, number of incoming links from other articles, etc.) and academic notability of the mentioned researchers. We also did not find any evidence that the scientists with better WP representation are necessarily more prominent in their fields. In addition, we inspected the Wikipedia coverage of notable scientists sampled from Thomson Reuters list of "highly cited researchers". In each of the examined fields, Wikipedia failed in covering notable scholars properly. Both findings imply that Wikipedia might be producing an inaccurate image of academics on the front end of science. By shedding light on how public perception of academic progress is formed, this study alerts that a subjective element might have been introduced into the hitherto structured system of academic evaluation.

Performance of general network coding nodes with stop-and-wait automatic repeat request transmission

The steady-state performance of general network coding node is investigated, when data are transmitted in packets based on the stop-and-wait automatic repeat request error-control scheme. The general network coding node considered has H number of incoming links that provide packets for forming the coded packets transmitted by one outgoing link. Each of the incoming and outgoing links is assumed to have some buffers for temporarily storing the data packets. The state transition of the general network coding node is modelled by a finite state machine, and its steady-state performance is derived based on the properties of discrete-time Markov chain. Specifically, the throughput, blocking probability and the distribution of contents are analysed, when the general network coding node is operated in the steady state. The performance of the general network coding node is investigated either by simulations or by evaluation of the formulas derived in this study. The performance results show that the analytical formulas obtained can be well justified by the simulation results. Furthermore, it can be shown that the packet error rate, the capacity of buffer and the number of incoming links may impose significant impact on the performance of general network coding node.

Better explanations of lexical and semantic cognition using networks derived from continued rather than single-word associations

In this article, we describe the most extensive set of word associations collected to date. The database contains over 12,000 cue words for which more than 70,000 participants generated three responses in a multiple-response free association task. The goal of this study was (1) to create a semantic network that covers a large part of the human lexicon, (2) to investigate the implications of a multiple-response procedure by deriving a weighted directed network, and (3) to show how measures of centrality and relatedness derived from this network predict both lexical access in a lexical decision task and semantic relatedness in similarity judgment tasks. First, our results show that the multiple-response procedure results in a more heterogeneous set of responses, which lead to better predictions of lexical access and semantic relatedness than do single-response procedures. Second, the directed nature of the network leads to a decomposition of centrality that primarily depends on the number of incoming links or in-degree of each node, rather than its set size or number of outgoing links. Both studies indicate that adequate representation formats and sufficiently rich data derived from word associations represent a valuable type of information in both lexical and semantic processing.

On the dynamics of random Boolean networks with scale-free outgoing connections

In the classical model of Random Boolean Networks (RBN) the number of incoming connections is the same for every node, while the distribution of outgoing links is Poissonian. These RBN are known to display two major dynamical behaviours, depending upon the value of some model parameters: an “ordered” and a “chaotic” regime. We introduce a modification of the classical way of building a RBN, which maintains the property that all the nodes have the same number of incoming links, but which gives rise to a scale-free distribution of outgoing connections. Because of this modification, the dynamical properties are deeply modified: the number of attractors is much smaller than in classical RBN, their length and the duration of the transients are shorter. Moreover, the number of different attractors is almost independent of the network size, over almost three orders of magnitudes (while in classical RBN this number grows with the size of the network). These results are based upon a detailed study of networks where each node has two input connections. A limited study of networks with three input connections per node shows that also in this case the number of attractors is almost independent of the network size.

Characterization of topological structure on complex networks.

Characterizing the topological structure of complex networks is a significant problem especially from the viewpoint of data mining on the World Wide Web. "Page rank" used in the commercial search engine Google is such a measure of authority to rank all the nodes matching a given query. We have investigated the page-rank distribution of the real Web and a growing network model, both of which have directed links and exhibit a power law distributions of in-degree (the number of incoming links to the node) and out-degree (the number of outgoing links from the node), respectively. We find a concentration of page rank on a small number of nodes and low page rank on high degree regimes in the real Web, which can be explained by topological properties of the network, e.g., network motifs, and connectivities of nearest neighbors.

Conceptualizing documentation on the Web: An evaluation of different heuristic‐based models for counting links between university Web sites

AbstractAll known previous Web link studies have used the Web page as the primary indivisible source document for counting purposes. Arguments are presented to explain why this is not necessarily optimal and why other alternatives have the potential to produce better results. This is despite the fact that individual Web files are often the only choice if search engines are used for raw data and are the easiest basic Web unit to identify. The central issue is of defining the Web “document”: that which should comprise the single indissoluble unit of coherent material. Three alternative heuristics are defined for the educational arena based upon the directory, the domain and the whole university site. These are then compared by implementing them on a set of 108 UK university institutional Web sites under the assumption that a more effective heuristic will tend to produce results that correlate more highly with institutional research productivity. It was discovered that the domain and directory models were able to successfully reduce the impact of anomalous linking behavior between pairs of Web sites, with the latter being the method of choice. Reasons are then given as to why a document model on its own cannot eliminate all anomalies in Web linking behavior. Finally, the results from all models give a clear confirmation of the very strong association between the research productivity of a UK university and the number of incoming links from its peers' Web sites.