Link Prediction on Complex Networks: An Experimental Survey.

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Data Science and Engineering Pub Date : 2022-01-01 Epub Date: 2022-06-21 DOI:10.1007/s41019-022-00188-2
Haixia Wu, Chunyao Song, Yao Ge, Tingjian Ge
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引用次数: 8

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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复杂网络的链路预测:实验研究。
复杂网络已被广泛用于模拟大量的关系。新冠肺炎疫情对现实世界的各种复杂网络产生了巨大影响,例如全球贸易网络、航空运输网络,甚至社会网络,这就是因疫情蔓延而引发的种族平等问题。链路预测在复杂网络分析中起着重要的作用,它可以通过分析现有的网络结构来发现网络中缺失的链路或预测网络中未来将出现的链路。因此,研究复杂网络上的链路预测问题就显得尤为重要。基于网络拓扑结构和实体属性的链路预测技术多种多样。本文提出了一种新的分类方法,将链接预测方法分为五类,并对这些方法进行了综述。近年来备受关注的基于网络嵌入的方法,特别是基于图神经网络的方法,也得到了创造性的研究。此外,我们分析了36个数据集,并根据其在真实网络中显示的拓扑特征将其分为7种类型的网络,并在这些网络上进行了全面的实验。我们进一步对实验结果进行了详细的分析,旨在找到最适合每种网络的方法。
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来源期刊
Data Science and Engineering
Data Science and Engineering Engineering-Computational Mechanics
CiteScore
10.40
自引率
2.40%
发文量
26
审稿时长
12 weeks
期刊介绍: The journal of Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital. The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains. Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area. It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering. More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to: (a) the nature and quality of data, (b) the computational complexity of data-intensive computing,(c) new methods for the design and analysis of the algorithms for solving problems with big data input,(d) collection and integration of data collected from internet and sensing devises or sensor networks, (e) representation, modeling, and visualization of  big data,(f)  storage, transmission, and management of big data,(g) methods and algorithms of  data intensive computing, such asmining big data,online analysis processing of big data,big data-based machine learning, big data based decision-making, statistical computation of big data, graph-theoretic computation of big data, linear algebraic computation of big data, and  big data-based optimization. (h) hardware systems and software systems for data-intensive computing, (i) data security, privacy, and trust, and(j) novel applications of big data.
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