Link Prediction Model Based on the Topological Feature Learning for Complex Networks

2020 ◽  
Vol 45 (12) ◽  
pp. 10051-10065
Author(s):  
Salam Jayachitra Devi ◽  
Buddha Singh
Keyword(s):  
Prediction Model ◽  
Complex Networks ◽  
Link Prediction ◽  
Feature Learning ◽  
Topological Feature ◽  
Model Based

Link prediction based on local weighted paths for complex networks

2017 ◽  
Vol 28 (04) ◽  
pp. 1750053
Author(s):  
Yabing Yao ◽  
Ruisheng Zhang ◽  
Fan Yang ◽  
Yongna Yuan ◽  
Rongjing Hu ◽  
...  
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Structural Similarity ◽  
Prediction Performance ◽  
Topological Feature ◽  
Topological Features ◽  
Node Similarity ◽  
Weighted Paths ◽  
Path Dependent

As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.

Ensemble-model-based link prediction of complex networks

2020 ◽  
Vol 166 ◽  
pp. 106978
Author(s):  
Kuanyang Li ◽  
Lilan Tu ◽  
Lang Chai
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Ensemble Model ◽  
Model Based

scholarly journals Spreading Predictability in Complex Networks

2021 ◽  
Author(s):  
Na Zhao ◽  
Jian Wang ◽  
Yong Yu ◽  
Junyan Zhao ◽  
Duanbing Chen
Keyword(s):  
Infectious Diseases ◽  
Prediction Model ◽  
Complex Networks ◽  
State Of The Art ◽  
Experimental Results ◽  
Vaccination Strategies ◽  
Model Based ◽  
The Future ◽  
Rumor Control

Abstract Many state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infected probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this paper, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future. Experimental results on synthetic and real networks demonstrate that the predicted infected individuals have rather consistency with the actual infected ones.

A Link Prediction Model Based on Similarity between Links

2014 ◽  
Vol 651-653 ◽  
pp. 1748-1752
Author(s):  
Fu Li Xie ◽  
Guang Quan Cheng
Keyword(s):  
Prediction Model ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Network Science ◽  
The Other ◽  
Prediction Methods ◽  
Good Prediction ◽  
Prediction Problem ◽  
Model Based ◽  
Good Prediction Accuracy

With the development of network science, the link prediction problem has attracted more and more attention. Among which, link prediction methods based on similarity has been most widely studied. Previous methods depicting similarity of nodes mainly consider their common neighbors. But in this paper, from the view of network environment of nodes, which is to analysis the links around the pair of nodes, derive nodes similarity through that of links, a new way to solve the link prediction problem is provided. This paper establishes a link prediction model based on similarity between links, presents the LE index. Finally, the LE index is tested on five real datasets, and compared with existing similarity-based link prediction methods, the experimental results show that LE index can achieve good prediction accuracy, especially outperforms the other methods in the Yeast network.

scholarly journals Link prediction model based on dynamic network representation

2020 ◽  
Vol 69 (16) ◽  
pp. 168901
Author(s):  
Zhong-Ming Han ◽  
Sheng-Nan Li ◽  
Chen-Ye Zheng ◽  
Da-Gao Duan ◽  
Wei-Jie Yang
Keyword(s):  
Prediction Model ◽  
Link Prediction ◽  
Dynamic Network ◽  
Model Based ◽  
Network Representation

scholarly journals Providing a Link Prediction Model based on Structural and Homophily Similarity in Social Networks

2020 ◽  
Vol 16 (4) ◽  
pp. 45-58
Author(s):  
Alireza Eshaghpoor ◽  
Mostafa Salehi ◽  
Vahid Ranjbar ◽  
◽  
◽  
...  
Keyword(s):  
Social Networks ◽  
Prediction Model ◽  
Link Prediction ◽  
Model Based

Comparitive Analysis of Link Prediction in Complex Networks

2021 ◽  
Vol 12 (3) ◽  
pp. 44-60
Author(s):  
Furqan Nasir ◽  
Haji Gul ◽  
Muhammad Bakhsh ◽  
Abdus Salam
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Complex Networks ◽  
Complex Network ◽  
Link Prediction ◽  
Jaccard Coefficient ◽  
Network Link ◽  
Multiple Prediction ◽  
Prediction Techniques ◽  
Link Formation

The most attractive aspect of data mining is link prediction in a complex network. Link prediction is the behavior of the network link formation by predicting missed and future relationships among elements based on current observed connections. Link prediction techniques can be categorized into probabilistic, similarity, and dimension reduction based. In this paper six familiar link predictors are applied on seven different network datasets to provide directory to users. The experimental results of multiple prediction algorithms were compared and analyzed on the basis of proposed comparative link prediction model. The results revealed that Jaccard coefficient and Hub promoted performed well on most of the datasets. Different applied methods are arranged on the basis of accuracy. Moreover, the shortcomings of different techniques are also presented.

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