Mining of Influencers in Signed Social Networks: A Memetic Approach

2018 ◽  
pp. 306-316
Author(s):  
Nancy Girdhar ◽  
K. K. Bharadwaj
Keyword(s):  
Social Networks ◽  
Signed Social Networks

Ranking Nodes in Signed Social Networks

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Moshen Shahriari ◽  
Mahdi Jalili
Keyword(s):  
Social Networks ◽  
Signed Social Networks

Partition signed social networks by spectral features and structural balance

2019 ◽  
Vol 33 (19) ◽  
pp. 1950211
Author(s):  
Xiaoyu Zhu ◽  
Yinghong Ma
Keyword(s):  
Social Networks ◽  
Social Systems ◽  
Structural Balance ◽  
The Social ◽  
Signed Social Networks ◽  
Spectral Algorithm ◽  
Community People ◽  
Signed Network ◽  
The Stability ◽  
The Relationship

In social networks, individuals are usually but not exactly divided into communities such that within each community people are friendly to each other while being hostile towards other communities. This is in line with structural balance theory which enables a comprehensive understanding of the stability and tensions of social systems. Yet, there may be some conflicts such as the intra-community negative edges or inter-community positive edges that affect the balancedness of the social system. This raises an interesting question of how to partition a signed network for minimal conflicts, i.e., maximum balancedness. In this paper, by analyzing the relationship between balancedness and spectrum space, we find that each eigenvector can be an indicator of dichotomous structure of networks. Incorporating the leader mechanism, we partition signed networks to maximize the balancedness with top-k eigenvectors. Moreover, we design an optimizing segment to further improve the balancedness of the network. Experimental data both from real social and synthetic networks demonstrate that the spectral algorithm has higher efficiency, robustness and scientificity.

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