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Getting solutions that are stringent and noteworthy to complex mathematical procedures through algorithms is not something new and has been a key for finding answers to tough questions. Such an algorithm extensively in use today is the PageRank algorithm. Proposed by Larry Page and Sergey Brin in 1996 and originally finding its roots in the Graph Theory, it has become a significant solution method in cases where complex chain analysis is involved. The algorithm makes use of basic yet intricate graph theory concepts, such as in-degrees and out-degrees corresponding to each node to give weights to a particular webpage and find its consequent rank or weight. This paper discusses the very basics of the algorithm. The paper also reviews the work done by Gabor Ivan and Vince Grolmusz, who, in 2010, used PageRank to determine the protein interaction chains in our body and rank them according to their corresponding importance. In addition, this paper also has provided a brief idea on another original research based on similarity-based methods for link prediction in complex networks. The driving formula for finding the PageRank value of a webpage is: Wi = d + ƩI = 1, I ≠ j Iij (Wi/ni). Brief elaboration on the significance of “d” or the damping factor in the above-mentioned formula by taking appropriate examples and through some light on the basic and structural working of the PageRank algorithm has also been done.

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Utilization of Personalized PageRank for Protein–Protein Interaction Analysis and Similarity-Based Complex Network Analysis: A Brief Review

  • 会议论文

  • 发表日期：

    2025年

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  • Arjab Sengupta,
  • Srijita Chakraborty,
  • Malay Gangopadhyay

摘要

Getting solutions that are stringent and noteworthy to complex mathematical procedures through algorithms is not something new and has been a key for finding answers to tough questions. Such an algorithm extensively in use today is the PageRank algorithm. Proposed by Larry Page and Sergey Brin in 1996 and originally finding its roots in the Graph Theory, it has become a significant solution method in cases where complex chain analysis is involved. The algorithm makes use of basic yet intricate graph theory concepts, such as in-degrees and out-degrees corresponding to each node to give weights to a particular webpage and find its consequent rank or weight. This paper discusses the very basics of the algorithm. The paper also reviews the work done by Gabor Ivan and Vince Grolmusz, who, in 2010, used PageRank to determine the protein interaction chains in our body and rank them according to their corresponding importance. In addition, this paper also has provided a brief idea on another original research based on similarity-based methods for link prediction in complex networks. The driving formula for finding the PageRank value of a webpage is: Wi = d + ƩI = 1, I ≠ j Iij (Wi/ni). Brief elaboration on the significance of “d” or the damping factor in the above-mentioned formula by taking appropriate examples and through some light on the basic and structural working of the PageRank algorithm has also been done.