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<p>Ranking is essential in information retrieval systems, as the web serves as a vast repository of both static and dynamic pages, providing an infinite source of information enriched with numerous hyperlinks. This wealth of data necessitates effective ranking to meet user needs, as search engines encompass a significant portion of web pages. The PageRank algorithm measures the importance and behavior of web pages, enhancing the relevance of search results through analysis of the web’s graph structure. In contrast, the Hypertext Induced Topic Search (HITS) algorithm operates on a framework of hubs and authorities, leveraging the rich context of hyperlinks. This paper analyzes web page importance using various ranking algorithms and introduces a novel algorithm, LinkRanker, to enhance ranking efficiency. Its performance is compared against established algorithms, including HITS, the Stochastic Approach for Link-Structure Analysis (SALSA), and Norm(p), with evaluations conducted on multiple hyperlink graphs.</p>

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Improving Ranking Efficiency in Information Retrieval: The LinkRanker Algorithm

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Kanta Prasad Sharma,
  • Mohammed Yousif Abo Keir,
  • Junainah Abd Hamid,
  • Gadug Sudhamsu,
  • Girish Paliwal,
  • Abhilasha Jadhav,
  • Pooja Rani,
  • Ahmed Alkhayyat

摘要

Ranking is essential in information retrieval systems, as the web serves as a vast repository of both static and dynamic pages, providing an infinite source of information enriched with numerous hyperlinks. This wealth of data necessitates effective ranking to meet user needs, as search engines encompass a significant portion of web pages. The PageRank algorithm measures the importance and behavior of web pages, enhancing the relevance of search results through analysis of the web’s graph structure. In contrast, the Hypertext Induced Topic Search (HITS) algorithm operates on a framework of hubs and authorities, leveraging the rich context of hyperlinks. This paper analyzes web page importance using various ranking algorithms and introduces a novel algorithm, LinkRanker, to enhance ranking efficiency. Its performance is compared against established algorithms, including HITS, the Stochastic Approach for Link-Structure Analysis (SALSA), and Norm(p), with evaluations conducted on multiple hyperlink graphs.