Vol. 12, Special Issue 7 (2023)

Web is a huge, massive, explosive, diverse, dynamic and mostly unstructured data repository. When user requests for a query on web then provide the relevant information to users for fulfill their needs. Everyone can store and retrieve the information from web. Extracting the important information from web is called web mining. Web mining aims to discover useful information or knowledge from the Web hyperlink structure, page content, and usage data. It uses many data mining techniques because of heterogeneity and semi-structured or unstructured nature of the Web data so can easily improve the web services in fast way. Web mining is used to categorize users and pages by analyzing user’s behavior, the content of pages and order of URLs accessed and then describe Web Structure mining. Web mining has three components: web content mining, web usage mining, web structure mining. This paper focuses on link Ranking algorithms in web structured mining and compares those algorithms which are used for information retrieval on web. Link algorithms are Page Rank (PR), WPR (Weighted Page Rank), HITS (Hyperlink-Induced Topic Search), Distance Rank, Eigen Rumor, and Dirichlet Rank algorithms.