Abstract
The use of online social networks has become a standard medium of social interactions and information spreading. Due to the significant amount of data available online, social network analysis has become apropos to the researchers of diverse domains to study and analyse innovative patterns, friendships, and relationships. Message dissemination through these networks is a complex and dynamic process. Moreover, the presence of reciprocal links intensify the whole process of propagation and expand the chances of reaching to the target node. We therefore empirically investigated the relative importance of reciprocal relationships in the directed social networks affecting information spreading. Since the dynamics of the information diffusion has considerable qualitative similarities with the spread of infections, we analysed six different variants of the Susceptible–Infected (SI) epidemic spreading model to evaluate the effect of reciprocity. By analysing three different directed networks on different network metrics using these variants, we establish the dominance of reciprocal links as compared to the non-reciprocal links. This study also contributes towards a closer examination of the subtleties responsible for maintaining the network connectivity.
Highlights
In recent years, information spreading on social networks has witnessed a massive surge due to the emergence of online social media as communication channels.In today’s virtual world, the exchange of information takes place through online social networks with the users as nodes and their relationships as the connectors between nodes
All nodes that receive the message m may choose to disseminate this message to their adjacent nodes with a probability p, which shows the willingness of the user to forward the message further
We evaluate the effects of reciprocal links on message diffusion by removing both reciprocal and non-reciprocal links, where a single reciprocal link corresponds to two non-reciprocal links [18] and calculate the total number of users who have received the given message m
Summary
Information spreading on social networks has witnessed a massive surge due to the emergence of online social media as communication channels.In today’s virtual world, the exchange of information takes place through online social networks with the users as nodes and their relationships as the connectors between nodes. Social network analysis (SNA) is a graph-theoretic based approach to understand and analyse human social interactions, wherein the users are interdependent actors, and links are the channels for flow of information [2]. SNA assists in determining the importance of structural relations to analyse observed behaviours and investigate community structure of other formal and informal networks [3]. Reciprocity is a behavioural response to perceived kindness and unkindness, where kindness comprises of both, the distributional fairness as well as intentional fairness. It is defined as the ratio of the number of bidirectional L↔ links, to the total number of links L [5].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
