Real-Time Influence Maximization in a RTB Setting

Dupuis, David; Mouza, Cédric Du; Travers, Nicolas; Chareyron, Gaël

doi:10.1007/s41019-020-00132-2

Cited by 9 publications

(2 citation statements)

References 38 publications

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“…The diffusion of information on social networks is a complex and dynamic process, which aroused the great research enthusiasm of researchers. The researches on information diffusion play an important role in many fields such as predicting how popular a piece of information will become [1,2,3,4], finding some nodes in a social network that could maximize the spread of influence [5,6,7], how much a cascade will grow [8,9] and so on. In this paper, we study the task of information diffusion prediction.…”

Section: Introductionmentioning

confidence: 99%

Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks

Xie,

Wang,

Jia

2021

Preprint

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The prediction for information diffusion on social networks has great practical significance in marketing and public opinion control. Cascade prediction aims to predict the individuals who will potentially repost the message on the social network. One kind of methods either exploit demographical, structural, and temporal features for prediction, or explicitly rely on particular information diffusion models. The other kind of models are fully data-driven and do not require a global network structure. Thus massive diffusion prediction models based on network embedding are proposed. These models embed the users into the latent space using their cascade information, but are lack of consideration for the intervene among users when embedding. In this paper, we propose an independent asymmetric embedding method to learn social embedding for cascade prediction. Different from existing methods, our method embeds each individual into one latent influence space and multiple latent susceptibility spaces. Furthermore, our method captures the co-occurrence regulation of user combination in cascades to improve the calculating effectiveness. The results of extensive experiments conducted on real-world datasets verify both the predictive accuracy and costeffectiveness of our approach.

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Section: Introductionmentioning

confidence: 99%

Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks

Xie,

Wang,

Jia

2021

Preprint

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“…Choosing influencers that can spread influence to a maximum number of audience with a minimum budget is widely studied in a field called influence maximization (IM) [4]. An IM algorithm is used to generate seeds set (influencers) that produces the best possible influence spread (the number of activated users) under specific diffusion models [5]. However, the commonly used diffusion models, i.e., linear threshold (LT) and independent cascade (IC), assume that each user has a similar level of influence degree and susceptibility.…”

Section: Introductionmentioning

confidence: 99%

Realistic influence maximization based on followers score and engagement grade on instagram

Purba

Yulia

2021

Bulletin EEI

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In recent years, the emergence of social media influencers attracts the study of a realistic influence maximization (IM) technique. The theoretical performance of IM has become matured. However, it is not enough since IM has to be implemented in a social media environment. Realistic IM algorithms and diffusion models have been proposed, such as the addition of user factors or a learning agent. However, most studies still relied on the influence spread benchmark, which makes the usefulness questionable. This research is among the first IM study using Instagram data. In this study, two diffusion models are proposed, which are based on the original IC and LT models, with the addition of the engagement grade (EG) factor. An algorithm called IMFS (IM with followers score) is proposed to accommodate the new models as well as IC and LT. In addition, realistic benchmark methods are proposed, namely the average engagement of the activated users, and the overlapping between post likers and activated users. The result shows that the proposed models are 2-3x more realistic if compared to IC and LT.

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Wind Energy System: Data Analysis and Operational Management

Khare

Khare²

2021

Handbook of Smart Materials, Technologies, and Devices

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Real-Time Influence Maximization in a RTB Setting

Cited by 9 publications

References 38 publications

Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks

Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks

Realistic influence maximization based on followers score and engagement grade on instagram

Wind Energy System: Data Analysis and Operational Management

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