Random walk-based ranking in signed social networks: model and algorithms

2019 ◽  
Vol 62 (2) ◽  
pp. 571-610
Author(s):  
Jinhong Jung ◽  
Woojeong Jin ◽  
U Kang
Keyword(s):  
Social Networks ◽  
Random Walk ◽  
Signed Social Networks

scholarly journals Supervised User Ranking in Signed Social Networks

2019 ◽  
Vol 33 ◽  
pp. 184-191
Author(s):  
Xiaoming Li ◽  
Hui Fang ◽  
Jie Zhang
Keyword(s):  
Social Networks ◽  
Random Walk ◽  
Loss Function ◽  
Local Structure ◽  
State Of The Art ◽  
Seed Node ◽  
Signed Social Networks ◽  
Ranking Model ◽  
Unrealistic Assumption ◽  
Personalized Ranking

The task of user ranking in signed networks, aiming to predict potential friends and enemies for each user, has attracted increasing attention in numerous applications. Existing approaches are mainly extended from heuristics of the traditional models in unsigned networks. They suffer from two limitations: (1) mainly focus on global rankings thus cannot provide effective personalized ranking results, and (2) have a relatively unrealistic assumption that each user treats her neighbors’ social strengths indifferently. To address these two issues, we propose a supervised method based on random walk to learn social strengths between each user and her neighbors, in which the random walk more likely visits “potential friends” and less likely visits “potential enemies”. We learn the personalized social strengths by optimizing on a particularly designed loss function oriented on ranking. We further present a fast ranking method based on the local structure among each seed node and a certain set of candidates. It much simplifies the proposed ranking model meanwhile maintains the performance. Experimental results demonstrate the superiority of our approach over the state-of-the-art approaches.

A note on implementation of a random-walk design to study adolescent social networks

1995 ◽  
Vol 17 (3-4) ◽  
pp. 251-255 ◽  
Author(s):  
Gene A. McGrady ◽  
Clementine Marrow ◽  
Gail Myers ◽  
Michael Daniels ◽  
Mildred Vera ◽  
...  
Keyword(s):  
Social Networks ◽  
Random Walk

Direction-Aware User Recommendation Based on Asymmetric Network Embedding

2022 ◽  
Vol 40 (2) ◽  
pp. 1-23
Author(s):  
Sheng Zhou ◽  
Xin Wang ◽  
Martin Ester ◽  
Bolang Li ◽  
Chen Ye ◽  
...  
Keyword(s):  
Social Networks ◽  
Random Walk ◽  
Real World ◽  
Directed Network ◽  
Network Embedding ◽  
The Social ◽  
Benchmark Datasets ◽  
Direction Information ◽  
User Recommendation ◽  
Asymmetric Proximity

User recommendation aims at recommending users with potential interests in the social network. Previous works have mainly focused on the undirected social networks with symmetric relationship such as friendship, whereas recent advances have been made on the asymmetric relationship such as the following and followed by relationship. Among the few existing direction-aware user recommendation methods, the random walk strategy has been widely adopted to extract the asymmetric proximity between users. However, according to our analysis on real-world directed social networks, we argue that the asymmetric proximity captured by existing random walk based methods are insufficient due to the inbalance in-degree and out-degree of nodes. To tackle this challenge, we propose InfoWalk, a novel informative walk strategy to efficiently capture the asymmetric proximity solely based on random walks. By transferring the direction information into the weights of each step, InfoWalk is able to overcome the limitation of edges while simultaneously maintain both the direction and proximity. Based on the asymmetric proximity captured by InfoWalk, we further propose the qualitative (DNE-L) and quantitative (DNE-T) directed network embedding methods, capable of preserving the two properties in the embedding space. Extensive experiments conducted on six real-world benchmark datasets demonstrate the superiority of the proposed DNE model over several state-of-the-art approaches in various tasks.

scholarly journals A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Chengying Mao ◽  
Weisong Xiao
Keyword(s):  
Social Networks ◽  
Random Walk ◽  
Preference Relation ◽  
Disease Outbreak ◽  
Centrality Measures ◽  
The Internet ◽  
Virus Propagation ◽  
Preference Graph ◽  
Partial Preference ◽  
Relation Analysis

In the era of big data, social network has become an important reflection of human communications and interactions on the Internet. Identifying the influential spreaders in networks plays a crucial role in various areas, such as disease outbreak, virus propagation, and public opinion controlling. Based on the three basic centrality measures, a comprehensive algorithm named PARW-Rank for evaluating node influences has been proposed by applying preference relation analysis and random walk technique. For each basic measure, the preference relation between every node pair in a network is analyzed to construct the partial preference graph (PPG). Then, the comprehensive preference graph (CPG) is generated by combining the preference relations with respect to three basic measures. Finally, the ranking of nodes is determined by conducting random walk on the CPG. Furthermore, five public social networks are used for comparative analysis. The experimental results show that our PARW-Rank algorithm can achieve the higher precision and better stability than the existing methods with a single centrality measure.

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