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.

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 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>

Reviews and a New Angle of β Coefficient Prediction Method

2013 ◽  
Vol 709 ◽  
pp. 687-690
Author(s):  
Jun Hang Liu
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Capital Market ◽  
Three Dimensional ◽  
Prediction Method ◽  
Prediction Methods ◽  
Nineteen Seventies ◽  
Entropy Measurement ◽  
Accuracy Of Prediction

The β coefficient is one of the key parameters in the capital market and risk management, and is the important decision-making basis for the stakeholders in the market economy. But from the nineteen seventies, β coefficient is unstable which has been confirmed by a number of literatures, therefore the follow-up researchers have always focused on searching for the better method to improve the accuracy of prediction of the β coefficient. This paper reviewed the existing literature about prediction methods for the β coefficient, and then gave a new method of forecasting the β coefficient-three dimensional entropy measurement.

Link prediction measures considering different neighbors’ effects and application in social networks

2017 ◽  
Vol 28 (03) ◽  
pp. 1750033 ◽  
Author(s):  
Peng Luo ◽  
Chong Wu ◽  
Yongli Li
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction ◽  
Prediction Models ◽  
Network Evolution ◽  
Social Characteristics ◽  
Prediction Methods ◽  
Proposed Model ◽  
Social Network Evolution ◽  
Artificial Network

Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

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.

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.

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