A survey on network node ranking algorithms: Representative methods, extensions, and applications
- PDF / 812,396 Bytes
- 11 Pages / 595.276 x 793.701 pts Page_size
- 20 Downloads / 297 Views
survey on network node ranking algorithms: Representative methods, extensions, and applications 1
2*
1
LIU JiaQi , LI XueRong & DONG JiChang 1
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China; 2 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China Received June 8, 2020; accepted June 28, 2020; published online October 14, 2020
The ranking of network node importance is one of the most essential problems in the field of network science. Node ranking algorithms serve as an essential part in many application scenarios such as search engine, social networks, and recommendation systems. This paper presents a systematic review on three representative methods: node ranking based on centralities, PageRank algorithm, and HITS algorithm. Furthermore, we investigate the latest extensions and improvements of these representative methods, provided with several main application fields. Inspired by the survey of current literatures, we attempt to propose promising directions for future research. The conclusions of this paper are enlightening and beneficial to both the academic and industrial communities. complex networks, node ranking methods, PageRank, HITS, algorithms Citation:
1
Liu J Q, Li X R, Dong J C. A survey on network node ranking algorithms: Representative methods, extensions, and applications. Sci China Tech Sci, 2020, 63, https://doi.org/10.1007/s11431-020-1683-2
Introduction
In the third decade of the 21st century, the world has almost reached the age of the internet of everything (IoE) [1]. In addition to the Internet, which constitutes every aspect of people’s lives, many things in the real world exist in the form of complex networks, such as social networks, ecological networks, and transportation networks. Network science has also become a very important research field today. Many research findings help people to better understand the composition, characteristics, and development of the complex network world. In the research field of network science, the ranking of network node importance is one of the most essential problems. In reality, many network-related application scenarios depend on the importance ranking of network nodes. The *Corresponding author (email: [email protected])
most famous example is the Google search engine, which ranks search results according to the importance of the page associated with the keywords. In social networks, messages from the accounts of the most influential public celebrities can be quickly propagandized to the whole network, so identifying the influential nodes is of great significance to the monitoring of public opinions and commercial public relations. In addition, the ranking of network nodes plays an important role in the fields of recommendation algorithms, scientific research evaluation, financial markets, and ecosystem protection. In recent years, the widely used ranking algorithm of network nodes is PageRank algorithm, which constitutes the core module of Google
Data Loading...
