2015
DOI: 10.14778/2735703.2735706
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Online topic-aware influence maximization

Abstract: 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 represented as topics) and are more likely to be influenced by their friends (or friends' friends) with similar topics. We … Show more

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Cited by 202 publications
(135 citation statements)
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“…The problem of identifying influentials has gained a lot of attention from several research communities as it has applications in viral marketing [12], [13], [14], disease prevention [15] and propagation [16], politics [17], [18], etc.…”
Section: A Identification Of Influential Usersmentioning
confidence: 99%
“…The problem of identifying influentials has gained a lot of attention from several research communities as it has applications in viral marketing [12], [13], [14], disease prevention [15] and propagation [16], politics [17], [18], etc.…”
Section: A Identification Of Influential Usersmentioning
confidence: 99%
“…(1) Topic-Aware and Competitive IM: The motive of topicaware IM [8,11,15] is to design strategies for maximizing the collective spread of information under a given topic-distribution. It allows different influence probabilities for each topic (assuming a joint distribution of topics), without considering the opinions (of a node) towards these topics.…”
Section: Related Workmentioning
confidence: 99%
“…In none of the works mentioned above, context information is considered. IM based on context information is studied in several other works such as [8], [10], [11]. However, these works assume that the influence probabilities are known and topics/contexts are discrete.…”
Section: A Influence Maximizationmentioning
confidence: 99%
“…Hence, the characteristics (context) of the product affects the influence probabilities. The strand of literature that is closest to the problem we consider in this paper in terms of the dependence of the influence probabilities on the context is called topic-aware IM [8]- [11]. To the best of our knowledge, none of the prior works in topic-aware IM develop learning algorithms with provable performance guarantees for the case when the influence probabilities are unknown.…”
Section: Introductionmentioning
confidence: 99%