scholarly journals Seven-Layer Model in Complex Networks Link Prediction: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6560
Author(s):  
Hui Wang ◽  
Zichun Le
Keyword(s):  
Complex Networks ◽  
Supervised Learning ◽  
Link Prediction ◽  
Complex Analysis ◽  
Auxiliary Information ◽  
Prediction Method ◽  
Prediction Methods ◽  
Layer Model ◽  
The Future ◽  
High Level

Link prediction is the most basic and essential problem in complex networks. This study analyzes the observed topological, time, attributive, label, weight, directional, and symbolic features and auxiliary information to find the lack of connection and predict the future possible connection. For discussion and analysis of the evolution of the network, the network model is of great significance. In the past two decades, link prediction has attracted extensive attention from experts in various fields, who have published numerous high-level papers, but few combine interdisciplinary characteristics. This survey analyzes and discusses the existing link prediction methods. The idea of stratification is introduced into the classification system of link prediction for the first time and proposes the design idea of a seven-layer model, namely the network, metadata, feature classification, selection input, processing, selection, and output layers. Among them, the processing layer divides link prediction methods into similarity-based, probabilistic, likelihood, supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning methods. The input features, evaluation metrics, complex analysis, experimental comparisons, relative merits, common dataset and open-source implementations for each link prediction method are then discussed in detail. Through analysis and comparison, we found that the link prediction method based on graph structure features has better prediction performance. Finally, the future development direction of link prediction in complex networks is discussed.

scholarly journals Study on a New Method of Link-Based Link Prediction in the Context of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chen Jicheng ◽  
Chen Hongchang ◽  
Li Hanchao
Keyword(s):  
Big Data ◽  
Supervised Learning ◽  
Link Prediction ◽  
Variance Reduction ◽  
Prediction Method ◽  
Accurate Information ◽  
Prediction Methods ◽  
Sparse Networks ◽  
Core Issues ◽  
Present World

Link prediction is a concept of network theory that intends to find a link between two separate network entities. In the present world of social media, this concept has taken root, and its application is seen through numerous social networks. A typical example is 2004, 4 February “TheFeacebook,” currently known as just Facebook. It uses this concept to recommend friends by checking their links using various algorithms. The same goes for shopping and e-commerce sites. Notwithstanding all the merits link prediction presents, they are only enjoyed by large networks. For sparse networks, there is a wide disparity between the links that are likely to form and the ones that include. A barrage of literature has been written to approach this problem; however, they mostly come from the angle of unsupervised learning (UL). While it may seem appropriate based on a dataset’s nature, it does not provide accurate information for sparse networks. Supervised learning could seem reasonable in such cases. This research is aimed at finding the most appropriate link-based link prediction methods in the context of big data based on supervised learning. There is a tone of books written on the same; nonetheless, they are core issues that are not always addressed in these studies, which are critical in understanding the concept of link prediction. This research explicitly looks at the new problems and uses the supervised approach in analyzing them to devise a full-fledge holistic link-based link prediction method. Specifically, the network issues that we will be delving into the lack of specificity in the existing techniques, observational periods, variance reduction, sampling approaches, and topological causes of imbalances. In the subsequent sections of the paper, we explain the theory prediction algorithms, precisely the flow-based process. We specifically address the problems on sparse networks that are never discussed with other prediction methods. The resolutions made by addressing the above techniques place our framework above the previous literature’s unsupervised approaches.

scholarly journals A novel similarity measure for missing link prediction in social networks

2020 ◽  
Vol 19 (2) ◽  
pp. 1071
Author(s):  
Gogulamudi Naga Chandrika ◽  
E. Srinivasa Reddy
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Similarity Measure ◽  
Network Theory ◽  
Link Prediction ◽  
Prediction Methods ◽  
Missing Link ◽  
The Social ◽  
Hidden Patterns ◽  
Over Time

<p><span>Social Networks progress over time by the addition of new nodes and links, form associations with one community to the other community. Over a few decades, the fast expansion of Social Networks has attracted many researchers to pay more attention towards complex networks, the collection of social data, understand the social behaviors of complex networks and predict future conflicts. Thus, Link prediction is imperative to do research with social networks and network theory. The objective of this research is to find the hidden patterns and uncovered missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new method is computed on common neighbors with node-to-node distance to get better accuracy of missing link prediction. </span><span>We tested the proposed measure on a variety of real-world linked datasets which are formed from various linked social networks. The proposed approach performance is compared with contemporary link prediction methods. Our measure makes very effective and intuitive in predicting disappeared links in linked social networks.</span></p>

Similarity indices based on link weight assignment for link prediction of unweighted complex networks

2017 ◽  
Vol 31 (02) ◽  
pp. 1650254 ◽  
Author(s):  
Shuxin Liu ◽  
Xinsheng Ji ◽  
Caixia Liu ◽  
Yi Bai
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Prediction Methods ◽  
Close Attention ◽  
Common Neighbor ◽  
Link Weight ◽  
Similarity Indices ◽  
Clustered Networks ◽  
Local Path

Many link prediction methods have been proposed for predicting the likelihood that a link exists between two nodes in complex networks. Among these methods, similarity indices are receiving close attention. Most similarity-based methods assume that the contribution of links with different topological structures is the same in the similarity calculations. This paper proposes a local weighted method, which weights the strength of connection between each pair of nodes. Based on the local weighted method, six local weighted similarity indices extended from unweighted similarity indices (including Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA), Salton, Jaccard and Local Path (LP) index) are proposed. Empirical study has shown that the local weighted method can significantly improve the prediction accuracy of these unweighted similarity indices and that in sparse and weakly clustered networks, the indices perform even better.

scholarly journals A Potential Information Capacity Index for Link Prediction of Complex Networks Based on the Cannikin Law

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 863 ◽  
Author(s):  
Xing Li ◽  
Shuxin Liu ◽  
Hongchang Chen ◽  
Kai Wang
Keyword(s):  
Complex Networks ◽  
Information Transmission ◽  
Link Prediction ◽  
Information Capacity ◽  
Prediction Methods ◽  
Transmission Process ◽  
Capacity Index ◽  
Definition Of ◽  
Definition Of Information ◽  
Transmission Capability

Recently, a number of similarity-based methods have been proposed for link prediction of complex networks. Among these indices, the resource-allocation-based prediction methods perform very well considering the amount of resources in the information transmission process between nodes. However, they ignore the information channels and their information capacity in information transmission process between two endpoints. Motivated by the Cannikin Law, the definition of information capacity is proposed to quantify the information transmission capability between any two nodes. Then, based on the information capacity, a potential information capacity (PIC) index is proposed for link prediction. Empirical study on 15 datasets has shown that the PIC index we proposed can achieve a good performance, compared with eight mainstream baselines.

Prediction of missing links and reconstruction of complex networks

2016 ◽  
Vol 27 (10) ◽  
pp. 1650120 ◽  
Author(s):  
Cheng-Jun Zhang ◽  
An Zeng
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
High Performance ◽  
Point Of View ◽  
Prediction Methods ◽  
Online Systems ◽  
Network Properties ◽  
High Prediction ◽  
Simple Link

Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

scholarly journals Research on Threat Information Network Based on Link Prediction

2021 ◽  
Vol 13 (2) ◽  
pp. 94-102
Author(s):  
Jin Du ◽  
Feng Yuan ◽  
Liping Ding ◽  
Guangxuan Chen ◽  
Xuehua Liu
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Situational Awareness ◽  
Prediction Method ◽  
Interdisciplinary Approach ◽  
Network Evolution ◽  
Information Network ◽  
Research Perspective ◽  
Intrinsic Factors ◽  
Node Similarity

The study of complex networks is to discover the characteristics of these connections and to discover the nature of the system between them. Link prediction method is a classic in the study of complex networks. It ca not only reflect the relationship between the node similarity. More can be estimated through the edge, which reveals the intrinsic factors of network evolution, namely the network evolution mechanism. Threat information network is the evolution and development of the network. The introduction of such a complex network of interdisciplinary approach is an innovative research perspective to observe that the threat intelligence occurs. The characteristics of the network show, at the same time, also can predict what will happen. The evolution of the network for network security situational awareness of the research provides a new approach.

scholarly journals Tourism Destination Preference Prediction Based on Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bin Deng ◽  
Jun Xu ◽  
Xin Wei
Keyword(s):  
Link Prediction ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Edge Computing ◽  
Prediction Methods ◽  
Random Forest Algorithm ◽  
Tourism Destination ◽  
Prediction Time ◽  
Weight Value ◽  
Destination Selection

In view of the fact that the important characteristics of tourism destination selection preference are not considered in the current prediction methods of tourism destination selection preference, resulting in low prediction accuracy and comprehensive accuracy and long prediction time, a tourism destination selection preference prediction method based on edge calculation is proposed. This paper uses edge computing to construct the characteristics of tourism destination selection preference and uses a random forest algorithm to select important features and carry out preliminary estimation and ranking. Using the multiple logit selection model, the tourists’ preference sequence for tourism destination selection is obtained and sorted and the tourism destination selection preference model is obtained. By calculating the weight value of tourism destination selection preference, the weight set of tourism destination selection preference is determined and the tourism destination selection preference is determined according to the link prediction method to realize the tourism destination selection preference prediction. The experimental results show that the comprehensive accuracy of the proposed method is good, which can effectively improve the prediction accuracy of tourism destination selection preference and shorten the prediction time of tourism destination selection preference.

Link prediction based on nonequilibrium cooperation effect

2018 ◽  
Vol 32 (11) ◽  
pp. 1850128 ◽  
Author(s):  
LanXi Li ◽  
XuZhen Zhu ◽  
Hui Tian
Keyword(s):  
Numerical Analysis ◽  
Theoretical Analysis ◽  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Prediction Method ◽  
Large Degree ◽  
Node Degree ◽  
Improve Accuracy

Link prediction in complex networks has become a common focus of many researchers. But most existing methods concentrate on neighbors, and rarely consider degree heterogeneity of two endpoints. Node degree represents the importance or status of endpoints. We describe the large-degree heterogeneity as the nonequilibrium between nodes. This nonequilibrium facilitates a stable cooperation between endpoints, so that two endpoints with large-degree heterogeneity tend to connect stably. We name such a phenomenon as the nonequilibrium cooperation effect. Therefore, this paper proposes a link prediction method based on the nonequilibrium cooperation effect to improve accuracy. Theoretical analysis will be processed in advance, and at the end, experiments will be performed in 12 real-world networks to compare the mainstream methods with our indices in the network through numerical analysis.

Predicting missing links in directed complex networks: A linear programming method

2020 ◽  
Vol 34 (29) ◽  
pp. 2050324
Author(s):  
Jin-Song Li ◽  
Jian-Hua Peng ◽  
Shu-Xin Liu ◽  
Zhi-Cheng Li
Keyword(s):  
Linear Programming ◽  
Complex Networks ◽  
Link Prediction ◽  
Statistical Physics ◽  
Information Science ◽  
Empirical Studies ◽  
Prediction Method ◽  
Challenging Problem ◽  
Linear Programming Method ◽  
State Of Art

Link prediction in complex networks has always been a hot topic in statistical physics, sociology and information science. Since most works focus on undirected networks, how to predict missing links in directed complex networks remains a valuable and challenging problem. Many existing methods fail to differentiate the information provided by links with different orientations, nor do they consider the unequal contributions of neighbors, leading to deficiency in prediction accuracy. In this paper, we propose a novel link prediction method in directed networks. It calculates the contributions of three types of neighbors by solving a simple linear programming problem. Empirical studies on eight real-world networks show that the proposed method performs better under two evaluation metrics in comparison with nine state-of-art benchmarks.

scholarly journals Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Fei Gao ◽  
Katarzyna Musial ◽  
Colin Cooper ◽  
Sophia Tsoka
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Prediction Method ◽  
Prediction Methods ◽  
Online Systems ◽  
Network Characteristics ◽  
High Prediction ◽  
The Right ◽  
Accuracy Of Prediction ◽  
Network Metrics

Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is theprediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

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