Overexposure-Aware Influence Maximization
نویسندگان
چکیده
منابع مشابه
Online Topic-Aware Influence Maximization
Influence maximization, whose objective is to select k users (called seeds) from a social network such that the number of users influenced by the seeds (called influence spread) is maximized, has attracted significant attention due to its widespread applications, such as viral marketing and rumor control. However, in real-world social networks, users have their own interests (which can be repre...
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Influence maximization is the key algorithmic problem behind viral marketing: it requires to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network, leading to a large number of adoptions. Although real world users evidently have di↵erent degrees of interest and authoritativeness on di↵erent topics, the bulk of...
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Most previous work on influence maximization in social networks assumes that the chosen influencers (or seed nodes) can be influenced with certainty (i.e., with no contingencies). In this paper, we focus on using influence maximization in public health domains for assisting low-resource communities, where contingencies are common. It is very difficult in these domains to ensure that the seed no...
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Background Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc...
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ژورنال
عنوان ژورنال: ACM Transactions on Internet Technology
سال: 2020
ISSN: 1533-5399,1557-6051
DOI: 10.1145/3408315