A preference random walk algorithm for link prediction through mutual influence nodes in complex networks
Predicting links in complex networks has been one of the essential topics within the realm of data mining and science discovery over the past few years. This problem remains an attempt to identify future, deleted, and redundant links using the existing links in a graph. Local random walk is consider...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-09-01
|
| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821001099 |
| _version_ | 1811310005285027840 |
|---|---|
| author | Kamal Berahmand Elahe Nasiri Saman Forouzandeh Yuefeng Li |
| author_facet | Kamal Berahmand Elahe Nasiri Saman Forouzandeh Yuefeng Li |
| author_sort | Kamal Berahmand |
| collection | DOAJ |
| description | Predicting links in complex networks has been one of the essential topics within the realm of data mining and science discovery over the past few years. This problem remains an attempt to identify future, deleted, and redundant links using the existing links in a graph. Local random walk is considered to be one of the most well-known algorithms in the category of quasi-local methods. It traverses the network using the traditional random walk with a limited number of steps, randomly selecting one adjacent node in each step among the nodes which have equal importance. Then this method uses the transition probability between node pairs to calculate the similarity between them. However, in most datasets this method is not able to perform accurately in scoring remarkably similar nodes. In the present article, an efficient method is proposed for improving local random walk by encouraging random walk to move, in every step, towards the node which has a stronger influence. Therefore, the next node is selected according to the influence of the source node. To do so, using mutual information, the concept of the asymmetric mutual influence of nodes is presented. A comparison between the proposed method and other similarity-based methods (local, quasi-local, and global) has been performed, and results have been reported for 11 real-world networks. It had a higher prediction accuracy compared with other link prediction approaches. |
| first_indexed | 2024-04-13T09:52:09Z |
| format | Article |
| id | doaj.art-9be3b26bfe3c4698bd88a5a6b390cf82 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T09:52:09Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-9be3b26bfe3c4698bd88a5a6b390cf822022-12-22T02:51:34ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-09-0134853755387A preference random walk algorithm for link prediction through mutual influence nodes in complex networksKamal Berahmand0Elahe Nasiri1Saman Forouzandeh2Yuefeng Li3Department of Science and Engineering, Queensland University of Technology, Brisbane, Australia; Corresponding author.Department of Information Technology and Communications, Azarbaijan Shahid Madani University, Tabriz, IranDepartment of Computer Engineering University of Applied Science and Technology, Center of Tehran Municipality ICT Org., Tehran, IranDepartment of Science and Engineering, Queensland University of Technology, Brisbane, AustraliaPredicting links in complex networks has been one of the essential topics within the realm of data mining and science discovery over the past few years. This problem remains an attempt to identify future, deleted, and redundant links using the existing links in a graph. Local random walk is considered to be one of the most well-known algorithms in the category of quasi-local methods. It traverses the network using the traditional random walk with a limited number of steps, randomly selecting one adjacent node in each step among the nodes which have equal importance. Then this method uses the transition probability between node pairs to calculate the similarity between them. However, in most datasets this method is not able to perform accurately in scoring remarkably similar nodes. In the present article, an efficient method is proposed for improving local random walk by encouraging random walk to move, in every step, towards the node which has a stronger influence. Therefore, the next node is selected according to the influence of the source node. To do so, using mutual information, the concept of the asymmetric mutual influence of nodes is presented. A comparison between the proposed method and other similarity-based methods (local, quasi-local, and global) has been performed, and results have been reported for 11 real-world networks. It had a higher prediction accuracy compared with other link prediction approaches.http://www.sciencedirect.com/science/article/pii/S1319157821001099Complex networkLink predictionLocal random walkBiased random walkMutual influence |
| spellingShingle | Kamal Berahmand Elahe Nasiri Saman Forouzandeh Yuefeng Li A preference random walk algorithm for link prediction through mutual influence nodes in complex networks Journal of King Saud University: Computer and Information Sciences Complex network Link prediction Local random walk Biased random walk Mutual influence |
| title | A preference random walk algorithm for link prediction through mutual influence nodes in complex networks |
| title_full | A preference random walk algorithm for link prediction through mutual influence nodes in complex networks |
| title_fullStr | A preference random walk algorithm for link prediction through mutual influence nodes in complex networks |
| title_full_unstemmed | A preference random walk algorithm for link prediction through mutual influence nodes in complex networks |
| title_short | A preference random walk algorithm for link prediction through mutual influence nodes in complex networks |
| title_sort | preference random walk algorithm for link prediction through mutual influence nodes in complex networks |
| topic | Complex network Link prediction Local random walk Biased random walk Mutual influence |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821001099 |
| work_keys_str_mv | AT kamalberahmand apreferencerandomwalkalgorithmforlinkpredictionthroughmutualinfluencenodesincomplexnetworks AT elahenasiri apreferencerandomwalkalgorithmforlinkpredictionthroughmutualinfluencenodesincomplexnetworks AT samanforouzandeh apreferencerandomwalkalgorithmforlinkpredictionthroughmutualinfluencenodesincomplexnetworks AT yuefengli apreferencerandomwalkalgorithmforlinkpredictionthroughmutualinfluencenodesincomplexnetworks AT kamalberahmand preferencerandomwalkalgorithmforlinkpredictionthroughmutualinfluencenodesincomplexnetworks AT elahenasiri preferencerandomwalkalgorithmforlinkpredictionthroughmutualinfluencenodesincomplexnetworks AT samanforouzandeh preferencerandomwalkalgorithmforlinkpredictionthroughmutualinfluencenodesincomplexnetworks AT yuefengli preferencerandomwalkalgorithmforlinkpredictionthroughmutualinfluencenodesincomplexnetworks |