An Improved Link Prediction Algorithm Based on Comprehensive Consideration of Joint Influence of Adjacent Nodes for Random Walk with Restart

2020 ◽  
Author(s):  
Liang Lv ◽  
Can Yi ◽  
Banglv Wu ◽  
Mingxuan Hu
Keyword(s):  
Random Walk ◽  
Link Prediction ◽  
Prediction Algorithm ◽  
Random Walk With Restart ◽  
Joint Influence

Random Walks On Biomedical Networks

2020 ◽  
Vol 17 ◽  
Author(s):  
Guiyang Zhang ◽  
Pan Wang ◽  
You Li ◽  
Guohua Huang
Keyword(s):  
Random Walk ◽  
Link Prediction ◽  
Drug Target ◽  
Target Identification ◽  
Point Of View ◽  
Random Walk With Restart ◽  
Function Annotation ◽  
Disease Biomarker ◽  
Drug Target Identification ◽  
Node Classification

Abstract: The biomedical network is becoming a fundamental tool to represent sophisticated bio-systems, while random walk models on it are becoming a sharp sword to address such challenging issues as gene function annotation, drug target identification, and disease biomarker recognition. Recently, numerous random walk models have been proposed and applied to biomedical networks. Due to good performances, the random walk is increasingly attracting more and more attention from multiple communities. In this survey, we firstly introduced various random walk models, with emphasis on the Pag-eRank and the random walk with restart. We then summarized applications of the RW on the biomedical networks from the graph learning point of view, which mainly included node classification, link prediction, cluster/community detection, and learning representation of the node. We discussed briefly its limitation and existing issues also

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

Optimized Random Walk with Restart for Recommendation Systems

2019 ◽  
pp. 320-332 ◽  
Author(s):  
Seyyed Mohammadreza Rahimi ◽  
Rodrigo Augusto de Oliveira e Silva ◽  
Behrouz Far ◽  
Xin Wang
Keyword(s):  
Random Walk ◽  
Recommendation Systems ◽  
Random Walk With Restart

scholarly journals Group Relationship Mining of College Students Based on Predictive Social Network

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Huazhang Liu
Keyword(s):  
College Students ◽  
Social Networks ◽  
Social Network ◽  
Complex Network ◽  
Link Prediction ◽  
Rapid Development ◽  
Combination Method ◽  
Prediction Algorithm ◽  
The Social ◽  
Random Block

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

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