Analysing Asymmetrical Associations using Fuzzy Graph and Discovering Hidden Connections in Facebook

2017 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Amita Jain ◽  
Sunny Rai ◽  
Ankita Manaktala ◽  
Lokender Sarna
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Hybrid Approach ◽  
Absolute Error ◽  
Fuzzy Graph ◽  
Relationship Strength ◽  
Relational Networks ◽  
Actual Outcome ◽  
The Relationship ◽  
Transactional Data

The fuzzy graph theory to analyse the relationship strength in Social Networks has gain significant potential in last few years and has seen applications in areas like Link Prediction, calculating Reciprocity, discovering central nodes etc. In this paper, we propose a framework to analyse and quantify the degree of strength of asymmetric relationships and predict hidden links in social networks using fuzzy logic. Till now, the work in fuzzy social relational networks has been limited to symmetric relationships. However, in this paper, we consider the scenario of asymmetric relations. The proposed approach is for web 2.0 application <em>Facebook</em>. Our contribution is three fold. First, the measurement of the strength of asymmetric relationship between nodes on the basis of social interaction using the concept of fuzzy graph. Second, a hybrid approach for prediction of missing links between two nodes on the basis of similarity of attributes of user profiles such as demographic, topology and network transactional data. Third, we perform fuzzy granular computing on attribute ‘strength of relationship’ and categorise into four granules namely <em>{socially close friends, socially near friends, socially far friends, socially very far friends}</em> based on the results of supervised learning conducted over dataset. Similarly, actual outcome for predicted links is categorised into three granules namely <em>Accept, Not accept and May be.</em> The proposed approach has predicted relationship strength with mean absolute error of 9.26% whereas the proposed approach for Link prediction has provided 64% correct predictions.

Resource Sharing and Allocation in Cloud Via Social Networks

2018 ◽  
Vol 7 (2.19) ◽  
pp. 66
Author(s):  
S Florence ◽  
C Shyamala Kumari ◽  
K Prema ◽  
L Leema Priyadarshini
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Social Network ◽  
Resource Sharing ◽  
Control Mechanisms ◽  
Relationship Strength ◽  
The People ◽  
The Social ◽  
Allocation Algorithms ◽  
The Relationship

Social network plays a major role in communication and interaction of the people. Nowadays, cloud computing is the emerging technology in the field of Information Technology. The allocation and sharing of resources can be possible in an innovative way, by combining the social networks processes and cloud computing concepts. In the social network cloud, based on the friend’s relationship strength the resources can be provided from one friend to another. Based on the clustering techniques the similar type of friends will be grouped and the resources will be allocated. The resource providers recommend the virtualized containers on their devices itself. For efficient allocation of resources the effective allocation algorithms are used. Based on the type of relationship they had, the relationship based access control mechanisms are provided.  

Extracting and Measuring Relationship Strength in Social Networks

2011 ◽  
pp. 178-192
Author(s):  
Steven Gustafson ◽  
Abha Moitra
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Design ◽  
Message Board ◽  
Ego Networks ◽  
Relationship Strength ◽  
The Relationship

This study examines how extracting relationships from data can lead to very different social networks. The chapter uses online message board data to define a relationship between two authors. After applying a threshold on the number of communications between members, the authors further constrain relationships to be supported by each member in the relationship also having a relationship to the same third member: the triangle constraint. By increasing the number of communications required to have a valid relationships between members, they see very different social networks being constructed. Authors find that the subtle design choices that are made when extracting relationships can lead to different networks, and that the variation itself could be useful for classifying and segmenting nodes in the network. For example, if a node is ‘central’ across different approaches to extracting relationships, one could assume with more confidence that the node is indeed ‘central’. Lastly, the chapter studies how future communication occurs between members and their ego-networks from prior data. By increasing the communication requirements to extract valid relationships, it is seen how future communication prediction is impacted and how social network design choices could be better informed by understanding these variations.

scholarly journals Resource Sharing and Allocation in Cloud Via Social Networks

2018 ◽  
Vol 7 (2.19) ◽  
pp. 66
Author(s):  
S Florence ◽  
C Shyamala Kumari ◽  
K Prema ◽  
L Leema Priyadarshini
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Social Network ◽  
Resource Sharing ◽  
Control Mechanisms ◽  
Relationship Strength ◽  
The People ◽  
The Social ◽  
Allocation Algorithms ◽  
The Relationship

Social network plays a major role in communication and interaction of the people. Nowadays, cloud computing is the emerging technology in the field of Information Technology. The allocation and sharing of resources can be possible in an innovative way, by combining the social networks processes and cloud computing concepts. In the social network cloud, based on the friend’s relationship strength the resources can be provided from one friend to another. Based on the clustering techniques the similar type of friends will be grouped and the resources will be allocated. The resource providers recommend the virtualized containers on their devices itself. For efficient allocation of resources the effective allocation algorithms are used. Based on the type of relationship they had, the relationship based access control mechanisms are provided.  

scholarly journals Hierarchical Social Recommendation Model Based on a Graph Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhongqin Bi ◽  
Lina Jing ◽  
Meijing Shan ◽  
Shuming Dou ◽  
Shiyang Wang
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Relationship ◽  
Recommendation Systems ◽  
Absolute Error ◽  
User Preferences ◽  
Social Recommendation ◽  
Public Datasets ◽  
The Relationship ◽  
Different Levels

With the continuous accumulation of social network data, social recommendation has become a widely used recommendation method. Based on the theory of social relationship propagation, mining user relationships in social networks can alleviate the problems of data sparsity and the cold start of recommendation systems. Therefore, integrating social information into recommendation systems is of profound importance. We present an efficient network model for social recommendation. The model is based on the graph neural network. It unifies the attention mechanism and bidirectional LSTM into the same framework and uses a multilayer perceptron. In addition, an embedded propagation method is added to learn the neighbor influences of different depths and extract useful neighbor information for social relationship modeling. We use this method to solve the problem that the current research methods of social recommendation only extract the superficial level of social networks but ignore the importance of the relationship strength of the users at different levels in the recommendation. This model integrates social relationships into user and project interactions, not only capturing the weight of the relationship between different users but also considering the influence of neighbors at different levels on user preferences. Experiments on two public datasets demonstrate that the proposed model is superior to other benchmark methods with respect to mean absolute error and root mean square error and can effectively improve the quality of recommendations.

Exploring the Relationship between Oral Health Social Networks, Attitude and Stage of change in Afterschool Staff

2013 ◽  
Author(s):  
Martha Oropeza ◽  
Tom Valente ◽  
Claudio Nigg ◽  
Jimmy Efird ◽  
Mikako Deguchi ◽  
...  
Keyword(s):  
Social Networks ◽  
Oral Health ◽  
Stage Of Change ◽  
The Relationship

Exploring the Relationship Between Oral Health Social Networks, Attitude and Stage of Change in Afterschool Staff

2013 ◽  
Author(s):  
Martha Oropeza ◽  
Thomas W. Valente ◽  
Claudio R. Nigg ◽  
Jimmy Efird ◽  
Mikako Deguchi ◽  
...  
Keyword(s):  
Social Networks ◽  
Oral Health ◽  
Stage Of Change ◽  
The Relationship

PLAYING WITH FIRE COLLECTIVELY: CONTEMPORARY CULTURAL RITES AS DEVISERS AND OUTCOMES OF COMMUNITY NETWORKS

2020 ◽  
Author(s):  
Alba Colombo ◽  
Jaime Altuna ◽  
Esther Oliver-Grasiot
Keyword(s):  
Social Networks ◽  
Social Context ◽  
Power Relations ◽  
Symbolic Capital ◽  
Community Networks ◽  
Mutual Dependence ◽  
Community Solidarity ◽  
Symbolic Content ◽  
The Relationship

Popular festivities and traditional events are important moments in which symbolic content, deep emotions and community solidarity are developed. However, there has been little research on the relationship between such events and their social networks and the power relations within these networks. This paper explores the ability of community events and networks to reflect and strengthen social context. Rather than observing the capacity of the event to generate a network, we focus on identifying how the event network is constructed, and how it creates relationships between the different groups, or nodes, within broader social networks. The case analysed is the Correfoc de la Mercè, a traditional firework event in Barcelona involving the Colles de diables, or Catalan popular fire culture groups. Our findings show that there is a bidirectional link or a mutual dependence between the groups (or nodes) and the event, which also support the development of shared social and symbolic capital.

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