Journal
DISCRETE MATHEMATICS ALGORITHMS AND APPLICATIONS
Volume 14, Issue 6, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S1793830922300016
Keywords
Social network analytics; community detection; link prediction; influence modeling; influence maximization; rumor blocking; viral marketing; influence maximization variants
Categories
Funding
- US National Science Foundation (NSF) [1822985]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1822985] Funding Source: National Science Foundation
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This paper explores the use of social media in generating Online Social Networks (OSNs) and the various research tasks associated with them, such as community detection, link prediction, and influence modeling. Different models for modeling influence diffusion processes are discussed, and the applications of influence maximization and rumor blocking in different scenarios are examined. The concept of viral marketing is also explored.
The use of social media has generated huge Online Social Networks (OSN). This network exhibits nice structural properties and has opened door to various research tasks like community detection, link prediction and influence modeling in OSN. There are several models such as linear threshold and independent cascade model proposed in the literature that models the influence diffusion process. Depending on the use-case one may want to amplify or attenuate information in OSN called influence maximization (IM) and rumor blocking (RB), respectively. In terms of marketing, it is called viral marketing. This paper extensively reviews the fundamental research tasks associated with the structure of OSN, groups them into different categories based on the fundamental research area discussed along with its applications. This paper also surveys RB and the various variants of IM found in the literature.
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