Abstract
Online social networks have nowadays become a buzzword for millions of users, who spend a lot of time online to remain in touch with other users by interacting online with them or to know about such other users’ likings and views about a movie, product, place, and so on. Thus, there is a considerable amount of information being spread among such online users which help in maximizing influence for a particular product, movie, holiday destination, etc. But, the main question remains as to how to identify the top few best influential users so as to help in promotion of any such a product or movie. This paper discusses about influence maximization in online social networks and also studies efficient techniques for the same. Considering time complexity as the prime factor for influence maximization techniques, this paper also aims to propose a new algorithm DegGreedy which yields a much faster output than the two basic standard influence maximization algorithms.
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Nandi, G., Sharma, U., Das, A. (2018). A Novel Hybrid Approach for Influence Maximization in Online Social Networks Based on Node Neighborhoods. In: Kalam, A., Das, S., Sharma, K. (eds) Advances in Electronics, Communication and Computing. Lecture Notes in Electrical Engineering, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-10-4765-7_54
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DOI: https://doi.org/10.1007/978-981-10-4765-7_54
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