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Stability and Robustness in Influence Maximization [PDF]
ACM Transactions on Knowledge Discovery From Data, 2018 In the well-studied Influence Maximization problem, the goal is to identify a set of k nodes in a social network whose joint influence on the network is maximized. A large body of recent work has justified research on Influence Maximization models and algorithms with their potential to create societal or economic ...
Xinran He
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Collective Influence Maximization
Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2019The omnipresence of cascading process in complex phenomena makes the identification of a small set of influential units, which is widely believed to trigger the outbreak, always an crucial issue in network science. Formulated as Influence maximization (IM) in 2003, this NP-hard problem has received a multitude of heuristic solutions with diverse angles.
Xudong Wu +5 more
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Influence maximization with partial feedback
Operations Research Letters, 2020Abstract The goal of Influence maximization (IM) is to select a set of most influential users in a social network subject to a budget constraint. In this work, we propose to study the adaptive IM problem under partial-feedback model. Our main contribution in this paper is to introduce a novel adaptive policy with bounded approximation ratio. One nice
Shaojie Tang 0001, Jing Yuan 0002
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A Survey on Influence Maximization Models
Expert Systems With ApplicationsMyriam Jaouadi, Lotfi Ben Romdhane
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No influence of ACTH on maximal performance
Psychopharmacology, 1995Although it is generally accepted in the sporting world that adrenocorticotropic hormones (ACTH) and corticosteroids enhance maximal performance, this claim has never been scientifically corroborated. In a counterbalanced, double-blind design, 1 mg ACTH or placebo was injected into 16 professional cyclists. They cycled for 1 h on a bicycle ergometer at
Soetens, Eric +2 more
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ACM Transactions on Knowledge Discovery from Data, 2020
Influence maximization problem attempts to find a small subset of nodes in a social network that makes the expected influence maximized, which has been researched intensively before. Most of the existing literature focus only on maximizing total influence, but it ignores whether the influential distribution is balanced through the network.
Jianxiong Guo, Weili Wu 0001
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Influence maximization problem attempts to find a small subset of nodes in a social network that makes the expected influence maximized, which has been researched intensively before. Most of the existing literature focus only on maximizing total influence, but it ignores whether the influential distribution is balanced through the network.
Jianxiong Guo, Weili Wu 0001
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2017
This chapter describes the prerequisites—independence and expertise—that intermediaries must possess to perform their crucial functions, exert power over producers and consumers, and influence the market. Independence has structural, economic, and cognitive dimensions.
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This chapter describes the prerequisites—independence and expertise—that intermediaries must possess to perform their crucial functions, exert power over producers and consumers, and influence the market. Independence has structural, economic, and cognitive dimensions.
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Influence Maximization in Trajectory Databases
IEEE Transactions on Knowledge and Data Engineering, 2017In this paper, we study a novel problem of influence maximization in trajectory databases that is very useful in precise location-aware advertising. It finds $k$ best trajectories to be attached with a given advertisement and maximizes the expected influence among a large group of audience. We show that the problem is NP-hard and propose both exact and
Long Guo +4 more
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Banzhaf Index for Influence Maximization
2015Social media has changed the way people communicate with each other and has brought people together. Enterprises are increasingly using it as a medium for marketing activities. However, due to the size of these networks, marketers often look for key customers (influencers) to drive the campaign to the community.
Balaji Vasan Srinivasan, Arava Sai Kumar
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Robustness to Dependency in Influence Maximization
Management ScienceIn this paper, we pursue a correlation-robust study of the influence maximization problem. Departing from the classic independent cascade model, we study a diffusion process adversarially adapted to the choice of seed set. More precisely, rather than the independent coupling of known individual edge probabilities, we now evaluate a seed set’s expected
Louis Chen +3 more
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