The week's pick
Reseach Article
HITS vs. PageRank: A Comparative analysis of Web Search Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 51 |
| Year of Publication: 2024 |
| Authors: Richard Osei Adu, Klinsman Kwaku Boateng, William Asiedu |
10.5120/ijca2024924177
|
Richard Osei Adu, Klinsman Kwaku Boateng, William Asiedu . HITS vs. PageRank: A Comparative analysis of Web Search Algorithms. International Journal of Computer Applications. 186, 51 ( Nov 2024), 32-36. DOI=10.5120/ijca2024924177
Abstract
This paper presents a comparative analysis of two prominent web search algorithms, Hyperlink-Induced Topic Search (HITS) and PageRank, which are widely used for ranking web pages in information retrieval systems. The study explores the theoretical foundations, algorithmic structures, performance metrics, and practical applications of both algorithms, highlighting their unique approaches to evaluating the importance of web pages. Using the Google Web Graph dataset and Cit-HepPh citation network, an empirical evaluation was conducted to assess the efficiency and effectiveness of HITS and PageRank in identifying key nodes within a network. The study evaluates their performance in ranking nodes, considering structural properties, correlation analysis, and score distributions. Results indicate that while PageRank ensures a balanced representation of node importance, HITS uniquely identifies key hubs and authorities. The findings reveal that while PageRank offers a more balanced distribution of page importance across a network, HITS effectively distinguishes between hubs and authorities, making it valuable for specific contexts like academic research and topic-specific searches. The low correlation between the scores of the two algorithms underscores their distinct methodologies and implications for search engine optimization. The paper concludes by recommending the use of each algorithm based on specific use cases and the nature of the web environment being analyzed.
References
- Khan, M., Mello, G. B. M., Engelstad, P., Habib, L., & Yazidi, A. (2022). HITS-GNN: A Simplified Propagation Scheme for Graph Neural Networks. 2022 IEEE International Conference on Big Data (Big Data).
- Leskovec, J., Lang, K. J., Dasgupta, A., & Mahoney, M. W. (2008). Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. arXiv (Cornell University).
- Mireia, Bolíbar. (2016). Macro, meso, micro: broadening the ‘social’ of social network analysis with a mixed methods approach. Quality & Quantity, 50(5):2217-2236.
- S. Deshmukh and K. Vishwakarma, (2021). "A Survey on Crawlers used in developing Search Engine," 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2021, pp. 1446-1452.
- Sharlene, Nagy, Hesse-Biber. (2010). Mixed Methods Research: Merging Theory with Practice.
- SNAP: Network datasets: Google web graph. (n.d.). https://snap.stanford.edu/data/web-Google.html
- Stoica, A. A., Litvak, N., & Chaintreau, A. (2024). Fairness Rising from the Ranks: HITS and PageRank on Homophilic Networks.
- Su, Y., Yi, Y., & Qin, J. (2019). The attack efficiency of PageRank and HITS algorithms on complex networks. International Journal of Embedded Systems, 11(3), 306.
- Weiss, G. M., Nguyen, N., Dominguez, K., & Leeds, D. D. (2021). Identifying Hubs in Undergraduate Course Networks Based on Scaled Co-Enrollments: Extended Version. arXiv (Cornell University).
- Zhang, X., & Wu, H. (2021). PageRank Algorithm and HITS Algorithm in Web Page Ranking. In Advances in intelligent systems and computing (pp. 389–395).
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