Hyperlink-Induced Topic Search (HITS; also known as Hubs and Authorities) is a link analysis algorithm that rates Web Pages. It was developed by Job Kleinberg, a professor in the Department of Computer Science at Cornell. The idea behind Hubs and Authorities stemmed from a particular insight into the creation of web pages when the Internet was originally forming; that is, certain web pages, known as hubs, served as large directories that were not actually authoritative in the information that it held, but were used as compilations of a broad catalog of information that led users directly to other authoritative pages. In other words, a good hub represented a page that pointed to many other pages, and a good authority represented a page that was linked by many different hubs. The scheme therefore assigns two scores for each page: its authority, which estimates the value of the content of the page, and its hub value, which estimates the value of its links to other pages. Authority and hub values are defined in terms of one another in a mutual recursion. An authority value is computed as the sum of the scaled hub values that point to that page. A hub value is the sum of the scaled authority values of the pages it points to. Some implementations also consider the relevance of the linked pages. HITS algorithm is in the same spirit as PageRank. They both make use of the link structure of the Web graph in order to decide the relevance of the pages. The difference is that unlike the PageRank algorithm, HITS only operates on a small sub-graph (the seed SQ) from the web graph. This sub-graph is query dependent; whenever we search with a different query phrase, the seed changes as well. HITS rank the seed nodes according to their authority and hub weights. The highest ranking pages are displayed to the user by the query engine. HITS Algorithm can exploit the plenitude of transistors present on the GPU. The SIMD architecture of the GPU can help to reduce the complexity of the algorithm. For any iteration, a single thread will be assigned to a single node on the Web Graph, which will compute the Hub Score for the node. Similarly, after the calculation of the Hub Score, the authority score will be computed. The Normalization process can also be executed in parallel. Hence the above three steps will use the computation strength to reduce the time required to process the authority pages for the given Web Graph. Algorithms such as Kleinberg‘s HITS algorithm, the PageRank algorithm of Brin and Page, and the SALSA algorithm of Lempel and Moran use the link structure of a network of webpages to assign weights to each page in the network. The weights can then be used to rank the pages as authoritative sources. These algorithms share a common underpinning; they find a dominant eigenvector of a non-negative matrix that describes the link structure of the given network and use the entries of this eigenvector as the page weights. We use this commonality to give a unified treatment, proving the existence of the required eigenvector for the PageRank, HITS, and SALSA algorithms, the uniqueness of the PageRank eigenvector, and the convergence of the algorithms to these eigenvectors. However, we show that the HITS and SALSA eigenvectors need not be unique. We examine how the initialization of the algorithms affects the final weightings produced. We give examples of networks that lead the HITS and SALSA algorithms to return non-unique or non-intuitive rankings. We characterize all such networks, in terms of the connectivity of the related HITS authority graph. We propose a modification, to HITS, using non-uniform distribution of authority and hub value. We prove that HITS returns a unique ranking, so long as the network is weakly connected.