A trust model for analysis of trust, influence and their relationship in social network communities

Influential nodes are capable of influencing the Social Network (SN) structure and functions. Influential nodes are not pre-defined and need to be identified to better understand and control the network. Trust of the nodes can be an important indicator of their influence and trusted nodes can also affect other nodes. However, analysis of trust and influence relationship in social network communities needs investigation. This study proposes a SNTrust model to find the trust of nodes in a network using a local and global trust and also investigates trust, influence and their relationship in SN communities. Different SN-based influence evaluation approaches named K-core, closeness centrality, eigenvector centrality, and page rank is used to find influential nodes. We have explored the trust and influence of nodes in their communities as well as in the network. Different standard datasets such as Consulting Company dataset, Freeman EIES dataset, Blogcatalogue dataset, and Facebook groups dataset are used for experimentation and Pearson's correlation and level of significance (p-value) are used for results evaluation. We found a positive linear correlation between the local trust of a node and its influence. It is found that the nodes which are trusted in the network are highly trusted in their communities. There is a strong linear relationship between the influence of a node in the network and community. Furthermore, it is also observed that nodes that are close to each other in a community have high trust among them. The results show that the proposed SNTrust model performed better in finding trust, influence and their correlation.