A Novel Hybrid Approach for Influence Maximization in Online Social Networks Based on Node Neighborhoods
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, p
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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.
Keywords Influence maximization Online social networks centrality Greedy algorithm DegGreedy algorithm
High-degree
1 Introduction Nowadays, accessing the Internet has become a part and parcel of everyone’s life, mainly due to the interest gained by online users in Online Social Networks (OSNs). These OSNs play a vital role in influencing the online users for making any G. Nandi (&) U. Sharma Assam Don Bosco University, Guwahati, Assam, India e-mail: [email protected] U. Sharma e-mail: [email protected] A. Das St. Anthony’s College, Shillong, Shillong, Meghalaya, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 A. Kalam et al. (eds.), Advances in Electronics, Communication and Computing, Lecture Notes in Electrical Engineering 443, https://doi.org/10.1007/978-981-10-4765-7_54
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kind of choices or decisions [2]. As for example, one online user can influence his/her online friends to make a decision on which holiday destination to visit, which songs to listen, which item to purchase, and so on. As a result the marketing agencies or companies are keen on finding the top few influential online customers who can be targeted for promotion of a particular product, movie, place, and so on. Any OSN can be graphically represented as shown in Fig. 1, in which the OSN consists of ten nodes or online users and connected by links or edges indicating the friendship or ties between them. Several researchers in the field of computer science have studied about influential maximization in OSNs to find few top best influential users in an OSN which are often called as seed sets. These seed sets play a major role in spreading information or influence with relate to a particular topic or product. Hence, influence maximization in OSNs is currently one of the most discussed topics in the field of Online Social Network (OSN) mining, in which the basic computational task is to select a set of initial seed set that can best influence more users in the network. In general, the influence maximization prob
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