Abstract
Social media plays a vital role in connecting people all around the world through various walks and phases of life, forming clustered meaningful communities. However, there is more scope for the social media platforms to mine fine-grained information that can entice and surprise the social media users based upon their respective egocentric networks. The list of mutual friends in an individual’s social network might be trivial or obvious most of the time. To up the game and surprise the individuals, the social media platforms could mine those mutual connections that are connected across different communities, serving as inter-cluster crucial edges between communities. As these connections are across the communities, the user possibly wouldn’t be aware of these connections and thus would be surprised to know them.
This work contributes along the lines of deploying community detection algorithms like Girvan Newman and graph based modelling techniques to produce the optimal number of surprise connections. This model was tested on real world Twitter based egocentric networks of 156 college students with evidence and survey, showcasing a good performance in surprising users,thereby increasing the interaction and engaging time of users on the social media platform significantly.
S. P. Mylavarapu and S. Govindarajan—Both the authors contributed equally to this work.
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Mylavarapu, S.P., Govindarajan, S. (2020). IDK My Friends: Link Analysis on Social Networks to Mine Surprise Connections. In: Balusamy, S., Dudin, A.N., Graña, M., Mohideen, A.K., Sreelaja, N.K., Malar, B. (eds) Computational Intelligence, Cyber Security and Computational Models. Models and Techniques for Intelligent Systems and Automation. ICC3 2019. Communications in Computer and Information Science, vol 1213. Springer, Singapore. https://doi.org/10.1007/978-981-15-9700-8_3
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