Abstract
Influence maximization in Online Social Networks (OSNs) is the task of finding a small subset of nodes, often called as seed nodes that could maximize the spread of influence in the network. With the success of OSNs such as Twitter, Facebook, Flickr and Flixster, the phenomenon of influence exerted by such online social network users on several other online users, and how it eventually propagates in the network, has recently caught the attention of computer researchers to be mainly applied in the marketing field. However, the enormous amount of nodes or users available in OSNs poses a great challenge for researchers to study such networks for influence maximization. In this paper, we study efficient influence maximization by comparing the general Greedy algorithm with two other centrality algorithms often used for this purpose.
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Nandi, G., Das, A. (2015). Efficient Spread of Influence in Online Social Networks. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 2. Smart Innovation, Systems and Technologies, vol 32. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2208-8_27
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DOI: https://doi.org/10.1007/978-81-322-2208-8_27
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