scholarly journals Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

2019 ◽  
Vol 4 (6) ◽  
pp. 244-254
Author(s):  
Elfadil Abdalla Mohameds ◽  
Nazar Zakis ◽  
Mohammad Marjans
Keyword(s):  
Social Networks ◽  
Literature Review ◽  
Link Prediction ◽  
Prediction Methods ◽  
Current Trends ◽  
Dynamic Social Networks

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>

scholarly journals A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 100
Author(s):  
Xinyu Huang ◽  
Dongming Chen ◽  
Tao Ren
Keyword(s):  
Social Networks ◽  
Gravity Model ◽  
Link Prediction ◽  
Temporal Networks ◽  
Scientific Society ◽  
Multidisciplinary Study ◽  
Dynamic Social Networks ◽  
Traditional Research ◽  
Real World Datasets ◽  
Social Applications

Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications.

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 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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