Link Prediction on Complex Networks: An Experimental Survey
Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused b...
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| Published in | Data Science and Engineering Vol. 7; no. 3; pp. 253 - 278 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Singapore
Springer Nature Singapore
01.09.2022
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2364-1185 2364-1541 2364-1541 |
| DOI | 10.1007/s41019-022-00188-2 |
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| Abstract | Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network. |
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| AbstractList | Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network. Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network.Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network. |
| Audience | Academic |
| Author | Song, Chunyao Ge, Yao Ge, Tingjian Wu, Haixia |
| Author_xml | – sequence: 1 givenname: Haixia surname: Wu fullname: Wu, Haixia organization: College of Computer Science, Tianjin Key Laboratory of Network and Data Security Technology, Nankai University – sequence: 2 givenname: Chunyao orcidid: 0000-0002-5715-5092 surname: Song fullname: Song, Chunyao email: chunyao.song@nankai.edu.cn organization: College of Computer Science, Tianjin Key Laboratory of Network and Data Security Technology, Nankai University – sequence: 3 givenname: Yao surname: Ge fullname: Ge, Yao organization: College of Computer Science, Tianjin Key Laboratory of Network and Data Security Technology, Nankai University – sequence: 4 givenname: Tingjian surname: Ge fullname: Ge, Tingjian organization: University of Massachusetts Lowell |
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| Keywords | Link prediction 99-00 00-01 Data mining Network analysis Complex networks |
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| SubjectTerms | Air transportation Algorithm Analysis and Problem Complexity Algorithms Artificial Intelligence Chemistry and Earth Sciences Computer Science Data Mining and Knowledge Discovery Database Management Epidemics Experiments Graph neural networks Graph representations International trade Network analysis Neural networks Physics Proteins Review/Survey Papers Social networks Statistics for Engineering Surveys Systems and Data Security Taxonomy Topology |
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| Title | Link Prediction on Complex Networks: An Experimental Survey |
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