scholarly journals Predicting Positive and Negative Relationships in Large Social Networks

PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0129530 ◽  
Author(s):  
Guan-Nan Wang ◽  
Hui Gao ◽  
Lian Chen ◽  
Dennis N. A. Mensah ◽  
Yan Fu
Keyword(s):  
Social Networks ◽  
Negative Relationships

A model of social network formation under the impact of structural balance

2016 ◽  
Vol 30 (09) ◽  
pp. 1650051 ◽  
Author(s):  
Pei Li ◽  
Jiajun Cheng ◽  
Yingwen Chen ◽  
Hui Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Formation ◽  
Common Phenomenon ◽  
Structural Balance ◽  
Negative Links ◽  
Social Network Formation ◽  
The Impact ◽  
Negative Relationships ◽  
Industrial Societies

Social networks have attracted remarkable attention from both academic and industrial societies and it is of great importance to understand the formation of social networks. However, most existing research cannot be applied directly to investigate social networks, where relationships are heterogeneous and structural balance is a common phenomenon. In this paper, we take both positive and negative relationships into consideration and propose a model to characterize the process of social network formation under the impact of structural balance. In this model, a new node first establishes a link with an existing node and then tries to connect to each of the newly connected node’s neighbors. If a new link is established, the type of this link is determined by structural balance. Then we analyze the degree distribution of the generated network theoretically, and estimate the fractions of positive and negative links. All analysis results are verified by simulations. These results are of importance to understand the formation of social networks, and the model can be easily extended to consider more realistic situations.

Analysis of opinion spreading in signed social networks under the impact of structural balance

2015 ◽  
Vol 29 (13) ◽  
pp. 1550079 ◽  
Author(s):  
Pei Li ◽  
Yini Zhang ◽  
Su He ◽  
Hui Wang
Keyword(s):  
Social Networks ◽  
Common Phenomenon ◽  
Structural Balance ◽  
User Influence ◽  
Signed Social Networks ◽  
Different Types ◽  
Negative Links ◽  
The Impact ◽  
Negative Relationships

Social networks have attracted remarkable attention and it is of great importance to understand the process of opinion spreading in popular social networks. However, most research on diffusion cannot be applied directly to investigate social networks, where relationships are heterogeneous and structural balance is a common phenomenon. In this paper, we propose models to characterize the process of opinion spreading in signed social networks under the impact of structural balance. We classify users into different types according to the numbers of their positive links, and define the term user influence to represent the average number of times that users are influenced, which is incurred by a user spreading an opinion. We then propose an approach to analyze the user influence theoretically and the analysis accuracy is verified by simulations. We observe that the user influence increases with user type and also increases with the fraction of negative links in the network if this fraction value exceeds some point. That's to say, negative relationships may enhance opinion spreading if we consider the impact of structural balance, which is an interesting result.

Finding the Trustworthiness Nodes from Signed Social Networks

2013 ◽  
Vol 22 (4) ◽  
pp. 471-485
Author(s):  
Hui Li ◽  
Shu Zhang ◽  
Xia Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Network ◽  
Network Services ◽  
Edge Weight ◽  
The Public ◽  
Signed Social Networks ◽  
Public Dataset ◽  
Negative Relationships ◽  
The Prestige

AbstractOnline social network services have brought a kind of new lifestyle to the world that is parallel to people’s daily offline activities. Social network analysis provides a useful perspective on a range of social computing applications. Social interaction on the Web includes both positive and negative relationships, which is certainly important to social networks. The authors of this article found that the accuracy of the signs of links in the underlying social networks can be predicted. The trust that other users impart on a node is an important attribute of networks. In this article, the authors present a model to compute the prestige of nodes in a trust-based network. The model is based on the idea that trustworthy nodes weigh more. To fulfill this task, the authors first attempt to infer the attitude of one user toward another by predicting signed edges in networks. Then, the authors propose an algorithm to compute the prestige and trustworthiness where the edge weight denotes the trust score. To prove the algorithm’s effectiveness, the authors conducted experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective.

Probabilistic Relation between Triadic Closure and the Balance of Social Networks in Presence of Influence

2015 ◽  
Vol 5 (4) ◽  
pp. 53-61
Author(s):  
Rahul Saha ◽  
G. Geetha ◽  
Gulshan Kumar
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Positive Relation ◽  
Major Research ◽  
Friendly Relation ◽  
Global View ◽  
The Social ◽  
Triadic Closure ◽  
Probabilistic Relation ◽  
Negative Relationships

Data analysis in social networking is a major research concern in todays' environment in the field of interpretation models of data for any network. Social networking includes only two types of relationships: firstly, a friendly relation with whom one is having a link that can be considered as a positive relation and secondly, a relationship with which one is not connected or so called one's enemies labelled as negative relationships. Balanced theorem of social networking claims that all the nodes in the social network can be divided into two sets: a friendship set and an enemy set and provides the global view of relationships. In this paper, the authors have shown a probabilistic model to show that the global view of social links does not only depend on negative and positive relations to be distinguished, but it also depends on influences parameters.

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