An Application of Improved PageRank in Focused Crawler

World Wide Web (WWW) is a platform that explores a wide range of information used for the development of web applications. Some examples of these applications include social network analysis, personalized item recommendations, and web page classification and ranking. Among these applications, search engines and web page ranking are particularly important as they consistently index and store billions of web pages on the internet. The main objective of this paper is to create an innovative framework for the classification and re-ranking of web pages using intelligent techniques. The framework is structured into two key phases: classification and re-ranking-based retrieval. In the initial classification phase, a series of pre-processing steps are implemented, including the elimination of HTML tags, punctuation, stop words, and the application of stemming. After pre-processing, a word-to-vector conversion is performed, followed by feature extraction utilizing Principal Component Analysis (PCA). This sequence of actions leads to optimal feature selection, which is vital for the precise classification of web pages. Given the multitude of features present in web pages that can compromise classification accuracy, this study employs a novel meta-heuristic algorithm, the Opposition Based-Tunicate Swarm Algorithm (O-TSA), to facilitate optimal feature selection. The refined features are subsequently processed through the Enhanced Convolutional-Recurrent Neural Network (E-CRNN), enhanced by O-TSA, resulting in the effective classification of diverse web page categories. In the second phase, the re-ranking process is executed using O-TSA, which establishes the objective function based on a similarity function (correlation) for URL matching, leading to optimal re-ranking of web.

<span lang="EN-US">Search engine optimization (SEO) is an important internet marketing strategy and process that facilitates maximizing an intended website’s visibility with search engine results. It is widely employed nowadays to improve traffic volume or quality from search engines to a particular website. Even though a significant number of publications imply the essential aspects of SEO, only a few provide generalized ideas to deal with the complex structure of the web. Also, the critical issues of content quality, site popularity, keyword density, and publicity factors were not much considered in the traditional ranking algorithms during SEO processes. This has negatively influenced the retrieval rate in the existing SEO techniques, and consequently, inadequate search results were obtained through search engines. Hence, the study considers web page ranking as a theoretical basis for the research and addresses these limitations in the existing system. It further improves SEO performance by introducing a unique web-page ranking strategic design to gain higher page rank results. The results of the investigational study show that the proposed system effectively contributes towards SEO with an improved page ranking strategy and also provides higher accuracy in calculating the importance score of web pages which is comparable with popular ranking algorithms such as hyperlink-induced topic search (HITS) and PageRank.</span>

Extrapolated splitting methods for multilinear PageRank computations

Web Crawlers ‘browse’ the World Wide Web (WWW) on behalf of search engine, to collect web pages from numerous collections of billions of documents. Metacrawler is similar to that of a meta search engine that combines the top web search results from popular search engines. World Wide Web is growing rapidly. This possesses great challenges to general purpose crawlers. This paper introduces an architectural framework of a Metacrawler. This crawler enables the user to retrieve information that is relevant to the topic from more than one traditional web search engines. The crawler works in such a way that it fetches only the pages that are relevant to the topic. The PageRank algorithm is often used in ranking web pages. But, the ranking causes the problem of topic-drift. So, modified PageRank algorithm is used to rank the retrieved web pages in such a way that it reduces this problem. The clustering method is used to combine the search results so that the user can easily select web pages from the clustered results based upon the requirement. Experimental results show the effectiveness of the Metacrawler.

The Web page rank algorithm is always regarded as the core of the search engine. Firstly, this article analyzes the traditional and classical rank algorithms briefly. Then, it proposes a new rank algorithm, which is called A-PageRank. In this algorithm, the PageRank value of the source page is distributed to its link-out pages according to the topic similarity. Lastly, a new method which uses both the similarity and divergence to weigh the match degree between one web page and one user query is adopted in order to increase the precision and recall rate of the search engine.

With the increased digital usage, web visibility has become critically essential for organizations when catering to a larger audience. This visibility on the web is directly related to web searches on search engines which is often governed by search engine optimization techniques liked link building and link farming amongst others. The current study identifies metrics for segregating websites for the purpose of link building for search engine optimization as it is important to invest resources in the right website sources. These metrics are further used for detecting websites outliers for effective optimization and subsequent search engine marketing. Two case studies of knowledge management portals from different domains are used having 1682 and 1070 websites respectively for validation of the proposed approach. The study evolutionary intelligence by proposing a k-means chaotic firefly algorithm coupled with k-nearest neighbor outlier detection for solving the problem. Factors like Page Rank, Page Authority, Domain Authority, Alexa Rank, Social Shares, Google Index and Domain Age emerge significant in the process. Further, the proposed chaotic firefly variants are compared to K-Means integrated firefly algorithm, bat algorithm and cuckoo search algorithm for accuracy and convergence showing comparable accuracy. Findings indicate that the convergence speeds are higher for proposed chaotic firefly approach for tuning absorption and attractiveness coefficients resulting in faster search for optimal cluster centroids. The proposed approach contributes both theoretically and methodologically in the domain of vendor selection for identifying genuine websites for avoiding investment on untrustworthy websites.

One of the key components which ensures the acceptance of web search service is the web page ranker - a component which is said to have been the main contributing factor to the early successes of Google. It is well established that a machine learning method such as the Graph Neural Network (GNN) is able to learn and estimate Google's page ranking algorithm. This paper shows that the GNN can successfully learn many other Web page ranking methods e.g. TrustRank, HITS and OPIC. Experimental results show that GNN may be suitable to learn any arbitrary web page ranking scheme, and hence, may be more flexible than any other existing web page ranking scheme. The significance of this observation lies in the fact that it is possible to learn ranking schemes for which no algorithmic solution exists or is known.

Nowadays, there are a lot of ontology construction methods, but they cannot express ontology clearly. In this paper, we compare the existing ontology construction methods and then summarize the advantages and disadvantages of these methods. Unlike previous work, we focus our attention on the fuzzy ontology and propose a semi-automatic method for constructing fuzzy domain ontology. Then we apply it to guide the crawler in sorting the web pages in the shipping field. The experimental results show that our method has a better effect on the ranking of web pages.

Attack graphs have been proposed as useful tools for analyzing security vulnerabilities in network systems. Even when they are produced efficiently, the size and complexity of attack graphs often prevent a human from fully comprehending the information conveyed. A distillation of this overwhelming amount of information is crucial to aid network administrators in efficiently allocating scarce human and financial resources. This paper introduces AssetRank, a generalization of Google’s PageRank algorithm which ranks web pages in web graphs. AssetRank addresses the unique semantics of dependency attack graphs and incorporates vulnerability data from public databases to compute metrics for the graph vertices (representing attacker privileges and vulnerabilities) which reveal their importance in attacks against the system. The results of applying the algorithm on a number of network scenarios show that the numeric ranks computed are consistent with the intuitive importance that the privileges and vulnerabilities have to an attacker. The vertex ranks can be used to prioritize countermeasures, help a human reader to better comprehend security problems, and provide input to further security analysis tools.Keywordsattack graphsecurity metricPageRankeigenvector

Network theory concepts form the core of algorithms that are designed to uncover valuable insights from various datasets. Especially, network centrality measures such as Eigenvector centrality, Katz centrality, PageRank centrality etc., are used in retrieving top-K viral information propagators in social networks,while web page ranking in efficient information retrieval, etc. In this paper, we propose a novel method for identifying top-K viral information propagators from a reduced search space. Our algorithm computes the Katz centrality and Local average centrality values of each node and tests the values against two threshold (constraints) values. Only those nodes, which satisfy these constraints, form the search space for top-K propagators. Our proposed algorithm is tested against four datasets and the results show that the proposed algorithm is capable of reducing the number of nodes in search space at least by 70%. We also considered the parameter (alpha and beta) dependency of Katz centrality values in our experiments and established a relationship between the alpha values, number of nodes in search space and network characteristics. Later, we compare the top-K results of our approach against the top-K results of degree centrality.

One of the main expectations of Web surfers is the inclusion of accurate and fresh result set in search engines' outcomes. Moreover, regarding the limited time and patience of nowadays Web users, they need to be advised on the significance of the Web domain they entered. Due to the exponentially growing and dynamic nature of the World Wide Web, considering these demands for Web crawlers with the existing Web page ranking and importance calculation methods are unachievable, expensive or time-consuming. Therefore, proposing novel heuristics in order to empower the search answer set is the concern of many researchers in this area. In this paper, we employ our previously proposed novel Web page importance metric of LogRank in order to calculate Web site significance value and to include the newly uploaded or infant Web pages into search result set to meet the mentioned expectations of Web surfers. We append our experiment on above matters through analyzing UTM University server log file and over UTM University Web domain.

Page rank is a ranking algorithm based on a random surfer model which is used in Google search engine and many other domains. Because of its initial success in Google search engine, page rank has become the de-facto choice when it comes to ranking nodes in a network structure. Despite the ubiquitous utility of the algorithm, little is known about the effect of topology on the performance of the page rank algorithm. Hence this paper discusses the performance of page rank algorithm under different topological conditions. We use scale-free networks and random networks along with a custom search engine we implemented in order to experimentally prove that the performance of page rank algorithm is deteriorated when the random network is perturbed. In contrast, scale-free topology is proven to be resilient against degree preserving perturbations which aids the page rank algorithm to deliver consistent results across multiple networks that are perturbed to varying proportions. Not only does the top ranking results emerge as stable nodes, but the overall performance of the algorithm is proven to be remarkably resilient which deepens our understanding about the risks in applying page rank algorithm without an initial analysis on the underlying network structure. The results conclusively suggests that while page rank algorithm can be applied to scale-free networks with relatively low risk, applying page rank algorithm to other topologies can be risky as well as misleading. Therefore, the success of the page rank algorithm in real world in search engines such as Google is at least partly due to the fact that the world wide web is a scale-free network. Since the world wide web is constantly evolving, we postulate that if the topological structure of the world wide web changes significantly so that it loses its scale-free nature to some extent, the page rank algorithm will not be as effective.

Micro-blog is a social tool of the new network era. It swept the world with its convenience of usage and real-time release of information. PageRank and Hits algorithms are the most widely used method in the evaluation of micro-blog's influence. But the two algorithms have the deficiency of poor performance and unrelated to specified keywords. We proposed a micro-blog enterprise users' reputation analysis model based on clustering network and improved PageRank algorithm. The model was simulated with the Sina micro-blog data acquired by a web crawler, and the model was compared with Hits and PageRank algorithm. The results show that the model has better convergence and its computational efficiency is superior to the traditional evaluation model based on PageRank or Hits algorithm.

In order to accurately identify the key lines in the photovoltaic (PV) grid-connected system, an identification method based on the improved PageRank algorithm is proposed. Firstly, the correlation matrix reflecting the electrical characteristics of the system is constructed using the line current-carrying rate, line breaking power flow transfer rate and line coupling rate, to replace the original network topology matrix. Secondly, through the entropy method, a comprehensive evaluation index based on electrical betweenness, load deviation rate and voltage shock rate is constructed to improve the distribution of the initial PageRank (PR) value of the PV grid-connected system. To study the changes’ impact of PVs active power outputs on the identification results of key lines in the Multi-PV power system, the HGWO-SVM (Hybrid Grey Wolves Optimized Support Vector Machine) algorithm was used to obtain the PVs daily outputs prediction curves and obtain fixed outputs of PVs at different periods, so as to study the impact of the variation of PV daily output on the key line identification. Taking the IEEE 39-node system containing multi-PV as an example, the identification results show that the improved PageRank algorithm is superior to the original method in line identification accuracy. The HGWO-SVM algorithm by adaptively modifying the cross operator and mutation operator also has a certain improvement in prediction accuracy. The changes of PVs daily outputs have different degree of influence on the line criticality (namely final PR value) during periods of high light intensity and other periods of light intensity.

Many existing page ranking algorithms are used in web mining to display the result in search engine result page. But these existing algorithms are either based on the inlinks and outlinks of the page or content of the page. It never consider the user interest on the page to calculate the page rank of that particular page. This leads to a need of web page ranking algorithm concerning content and usage of the pages. Proposed algorithm focuses on title, Meta, H1 and paragraph tags to find the similarity of the page. Tag Analyzer Algorithm is used to analyze these tags' content. Besides, it considers how long the user stays in that page to compute user interest score. TPPR technique computes the score in two phases. Based on the output of TPPR algorithm, the URLs are sequenced and displayed to the user. Event Explore technique detects whether the user is idle or active on the page. The proposed algorithm produced better performance and displays the most relevant web pages in the top of the result. From the results, "Two Phase Page Ranking" (TPPR) algorithm is better than PR algorithm of taken data set. TPPR algorithm can be used in web mining to improve the ranking system of search engine.