Link prediction based on local information considering preferential attachment

2016 ◽  
Vol 443 ◽  
pp. 537-542 ◽  
Author(s):  
Shan Zeng
Keyword(s):  
Link Prediction ◽  
Preferential Attachment ◽  
Local Information

scholarly journals JKRW Link Prediction – A New Ensemble Technique Based on Merging Other Known Techniques in The Social Network Analysis

2021 ◽  
Vol 15 (12) ◽  
pp. 125
Author(s):  
Aya Taleb ◽  
Rizik M. H. Al-Sayyed ◽  
Hamed S. Al-Bdour
Keyword(s):  
Link Prediction ◽  
Preferential Attachment ◽  
Large Data ◽  
The Other ◽  
Jaccard Coefficient ◽  
Ensemble Technique ◽  
Signed Rank ◽  
Proposed Model ◽  
Different Populations ◽  
A New Technique

In this research, a new technique to improve the accuracy of the link prediction for most of the networks is proposed; it is based on the prediction ensemble approach using the voting merging technique. The new proposed ensemble called Jaccard, Katz, and Random models Wrapper (JKRW), it scales up the prediction accuracy and provides better predictions for different sizes of populations including small, medium, and large data. The proposed model has been tested and evaluated based on the area under curve (AUC) and accuracy (ACC) measures. These measures applied to the other models used in this study that has been built based on the Jaccard Coefficient, Katz, Adamic/Adar, and Preferential attachment. Results from applying the evaluation matrices verify the improvement of JKRW effectiveness and stability in comparison to the other tested models.  The results from applying the Wilcoxon signed-rank method (one of the non-parametric paired tests) indicate that JKRW has significant differences compared to the other models in the different populations at <strong>0.95</strong> confident interval.

scholarly journals Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships

2020 ◽  
Vol 19 ◽  
pp. 117693512094221
Author(s):  
Shahab Bakhtiari ◽  
Sadegh Sulaimany ◽  
Mehrdad Talebi ◽  
Kabmiz Kalhor
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Link Prediction ◽  
Molecular Mechanisms ◽  
Preferential Attachment ◽  
Small Cell ◽  
Bipartite Network ◽  
Small Cell Lung ◽  
Single Nucleotide

Genetic variations such as single nucleotide polymorphisms (SNPs) can cause susceptibility to cancer. Although thousands of genetic variants have been identified to be associated with different cancers, the molecular mechanisms of cancer remain unknown. There is not a particular dataset of relationships between cancer and SNPs, as a bipartite network, for computational analysis and prediction. Link prediction as a computational graph analysis method can help us to gain new insight into the network. In this article, after creating a network between cancer and SNPs using SNPedia and Cancer Research UK databases, we evaluated the computational link prediction methods to foresee new SNP-Cancer relationships. Results show that among the popular scoring methods based on network topology, for relation prediction, the preferential attachment (PA) algorithm is the most robust method according to computational and experimental evidence, and some of its computational predictions are corroborated in recent publications. According to the PA predictions, rs1801394-Non-small cell lung cancer, rs4880-Non-small cell lung cancer, and rs1805794-Colorectal cancer are some of the best probable SNP-Cancer associations that have not yet been mentioned in any published article, and they are the most probable candidates for additional laboratory and validation studies. Also, it is feasible to improve the predicting algorithms to produce new predictions in the future.

scholarly journals Link Prediction and Node Classification Based on Multitask Graph Autoencoder

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shicong Chen ◽  
Deyu Yuan ◽  
Shuhua Huang ◽  
Yang Chen
Keyword(s):  
Loss Function ◽  
Link Prediction ◽  
Deep Level ◽  
Preferential Attachment ◽  
Similarity Index ◽  
Representation Learning ◽  
High Order ◽  
Fine Tuning ◽  
Common Neighbor ◽  
Node Classification

The goal of network representation learning is to extract deep-level abstraction from data features that can also be viewed as a process of transforming the high-dimensional data to low-dimensional features. Learning the mapping functions between two vector spaces is an essential problem. In this paper, we propose a new similarity index based on traditional machine learning, which integrates the concepts of common neighbor, local path, and preferential attachment. Furthermore, for applying the link prediction methods to the field of node classification, we have innovatively established an architecture named multitask graph autoencoder. Specifically, in the context of structural deep network embedding, the architecture designs a framework of high-order loss function by calculating the node similarity from multiple angles so that the model can make up for the deficiency of the second-order loss function. Through the parameter fine-tuning, the high-order loss function is introduced into the optimized autoencoder. Proved by the effective experiments, the framework is generally applicable to the majority of classical similarity indexes.

Link prediction based on local major path degree

2018 ◽  
Vol 32 (29) ◽  
pp. 1850348
Author(s):  
Xu-Hua Yang ◽  
Xuhua Yang ◽  
Fei Ling ◽  
Hai-Feng Zhang ◽  
Duan Zhang ◽  
...  
Keyword(s):  
Data Mining ◽  
Biological Networks ◽  
Link Prediction ◽  
Prediction Performance ◽  
Local Information ◽  
Network Nodes ◽  
And Topology ◽  
Similarity Indices ◽  
High Prediction ◽  
Similarity Information

Link prediction can estimate the probablity of the existence of an unknown or future edges between two arbitrary disconnected nodes (two seed nodes) in complex networks on the basis of information regarding network nodes, edges and topology. With the important practical value in many fields such as social networks, electronic commerce, data mining and biological networks, link prediction is attracting considerable attention from scientists in various fields. In this paper, we find that degree distribution and strength of two- and three-step local paths between two seed nodes can reveal effective similarity information between the two nodes. An index called local major path degree (LMPD) is proposed to estimate the probability of generating a link between two seed nodes. To indicate the efficiency of this algorithm, we compare it with nine well-known similarity indices based on local information in 12 real networks. Results show that the LMPD algorithm can achieve high prediction performance.

Relative Assortativity Index: A Quantitative Metric to Assess the Impact of Link Prediction Techniques on Assortativity of Complex Networks

2019 ◽  
Vol 63 (9) ◽  
pp. 1417-1437
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Biological Networks ◽  
Link Prediction ◽  
Preferential Attachment ◽  
Prediction Technique ◽  
Prediction Techniques ◽  
The Impact

Abstract We propose a quantitative metric (called relative assortativity index, RAI) to assess the extent with which a real-world network would become relatively more assortative due to link addition(s) using a link prediction technique. Our methodology is as follows: for a link prediction technique applied on a particular real-world network, we keep track of the assortativity index values incurred during the sequence of link additions until there is negligible change in the assortativity index values for successive link additions. We count the number of network instances for which the assortativity index after a link addition is greater or lower than the assortativity index prior to the link addition and refer to these counts as relative assortativity count and relative dissortativity count, respectively. RAI is computed as (relative assortativity count − relative dissortativity count) / (relative assortativity count + relative dissortativity count). We analyzed a suite of 80 real-world networks across different domains using 3 representative neighborhood-based link prediction techniques (Preferential attachment, Adamic Adar and Jaccard coefficients [JACs]). We observe the RAI values for the JAC technique to be positive and larger for several real-world networks, while most of the biological networks exhibited positive RAI values for all the three techniques.

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