A Survey on Personalized PageRank Computation Algorithms
Personalized PageRank (PPR) is an important variation of PageRank, which is a widely applied popularity measure for Web search. Unlike the original PageRank, PPR is a node proximity measure that represents the degree of closeness among multiple nodes within a graph. It is also widely applied to dive...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8895775/ |
| _version_ | 1819169960227766272 |
|---|---|
| author | Sungchan Park Wonseok Lee Byeongseo Choe Sang-Goo Lee |
| author_facet | Sungchan Park Wonseok Lee Byeongseo Choe Sang-Goo Lee |
| author_sort | Sungchan Park |
| collection | DOAJ |
| description | Personalized PageRank (PPR) is an important variation of PageRank, which is a widely applied popularity measure for Web search. Unlike the original PageRank, PPR is a node proximity measure that represents the degree of closeness among multiple nodes within a graph. It is also widely applied to diverse domains, such as information retrieval, recommendations, and knowledge discovery, due to its theoretical simplicity and flexibility. However, computing PPR in large graphs using naïve algorithms such as iterative matrix multiplication and matrix inversion is not fast enough for many of these applications. Therefore, devising efficient PPR algorithms has been one of the most important subjects in large-scale graph processing. In this paper, we review the algorithms for efficient PPR computations, organizing them into five categories based on their core ideas. Along with detailed explanations including recent advances and their applications, we provide a multifaceted comparison of the algorithms. |
| first_indexed | 2024-12-22T19:27:48Z |
| format | Article |
| id | doaj.art-225d019de4a9445ba1065faed7f17c4c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:27:48Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-225d019de4a9445ba1065faed7f17c4c2022-12-21T18:15:12ZengIEEEIEEE Access2169-35362019-01-01716304916306210.1109/ACCESS.2019.29526538895775A Survey on Personalized PageRank Computation AlgorithmsSungchan Park0https://orcid.org/0000-0002-6855-3643Wonseok Lee1Byeongseo Choe2Sang-Goo Lee3Department of Computer Science and Engineering, Seoul National University, Seoul, South KoreaDepartment of Computer Science and Engineering, Seoul National University, Seoul, South KoreaDepartment of Computer Science and Engineering, Seoul National University, Seoul, South KoreaDepartment of Computer Science and Engineering, Seoul National University, Seoul, South KoreaPersonalized PageRank (PPR) is an important variation of PageRank, which is a widely applied popularity measure for Web search. Unlike the original PageRank, PPR is a node proximity measure that represents the degree of closeness among multiple nodes within a graph. It is also widely applied to diverse domains, such as information retrieval, recommendations, and knowledge discovery, due to its theoretical simplicity and flexibility. However, computing PPR in large graphs using naïve algorithms such as iterative matrix multiplication and matrix inversion is not fast enough for many of these applications. Therefore, devising efficient PPR algorithms has been one of the most important subjects in large-scale graph processing. In this paper, we review the algorithms for efficient PPR computations, organizing them into five categories based on their core ideas. Along with detailed explanations including recent advances and their applications, we provide a multifaceted comparison of the algorithms.https://ieeexplore.ieee.org/document/8895775/Personalized PageRankgraph data mininggraph Analysis |
| spellingShingle | Sungchan Park Wonseok Lee Byeongseo Choe Sang-Goo Lee A Survey on Personalized PageRank Computation Algorithms IEEE Access Personalized PageRank graph data mining graph Analysis |
| title | A Survey on Personalized PageRank Computation Algorithms |
| title_full | A Survey on Personalized PageRank Computation Algorithms |
| title_fullStr | A Survey on Personalized PageRank Computation Algorithms |
| title_full_unstemmed | A Survey on Personalized PageRank Computation Algorithms |
| title_short | A Survey on Personalized PageRank Computation Algorithms |
| title_sort | survey on personalized pagerank computation algorithms |
| topic | Personalized PageRank graph data mining graph Analysis |
| url | https://ieeexplore.ieee.org/document/8895775/ |
| work_keys_str_mv | AT sungchanpark asurveyonpersonalizedpagerankcomputationalgorithms AT wonseoklee asurveyonpersonalizedpagerankcomputationalgorithms AT byeongseochoe asurveyonpersonalizedpagerankcomputationalgorithms AT sanggoolee asurveyonpersonalizedpagerankcomputationalgorithms AT sungchanpark surveyonpersonalizedpagerankcomputationalgorithms AT wonseoklee surveyonpersonalizedpagerankcomputationalgorithms AT byeongseochoe surveyonpersonalizedpagerankcomputationalgorithms AT sanggoolee surveyonpersonalizedpagerankcomputationalgorithms |