Deepinf: Social influence prediction with deep learning
Social and information networking activities such as on Facebook, Twitter, WeChat, and
Weibo have become an indispensable part of our everyday life, where we can easily access …
Weibo have become an indispensable part of our everyday life, where we can easily access …
Deep graph representation learning and optimization for influence maximization
Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …
network to maximize the expected number of influenced users. Researchers have made …
Personalized DeepInf: enhanced social influence prediction with deep learning and transfer learning
CK Leung, A Cuzzocrea, JJ Mai… - … conference on big …, 2019 - ieeexplore.ieee.org
Social influence is referred to as the phenomenon that one's opinions or behaviors be
affected by others. Nowadays, the potential impact of social influence analysis (SIA) is …
affected by others. Nowadays, the potential impact of social influence analysis (SIA) is …
Inf2vec: Latent representation model for social influence embedding
As a fundamental problem in social influence propagation analysis, learning influence
parameters has been extensively investigated. Most of the existing methods are proposed to …
parameters has been extensively investigated. Most of the existing methods are proposed to …
Deep reinforcement learning-based approach to tackle topic-aware influence maximization
S Tian, S Mo, L Wang, Z Peng - Data Science and Engineering, 2020 - Springer
Motivated by the application of viral marketing, the topic-aware influence maximization (TIM)
problem has been proposed to identify the most influential users under given topics. In …
problem has been proposed to identify the most influential users under given topics. In …
Deepis: Susceptibility estimation on social networks
Influence diffusion estimation is a crucial problem in social network analysis. Most prior
works mainly focus on predicting the total influence spread, ie, the expected number of …
works mainly focus on predicting the total influence spread, ie, the expected number of …
Learning influence probabilities in social networks
Recently, there has been tremendous interest in the phenomenon of influence propagation
in social networks. The studies in this area assume they have as input to their problems a …
in social networks. The studies in this area assume they have as input to their problems a …
Learning influence from heterogeneous social networks
Influence is a complex and subtle force that governs social dynamics and user behaviors.
Understanding how users influence each other can benefit various applications, eg, viral …
Understanding how users influence each other can benefit various applications, eg, viral …
Network dynamic GCN influence maximization algorithm with leader fake labeling mechanism
C Zhang, W Li, D Wei, Y Liu, Z Li - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Influence maximization is an important technique for its significant value on various social
network applications, such as viral marketing, advertisement, and recommendation …
network applications, such as viral marketing, advertisement, and recommendation …
Mining topic-level influence in heterogeneous networks
Influence is a complex and subtle force that governs the dynamics of social networks as well
as the behaviors of involved users. Understanding influence can benefit various applications …
as the behaviors of involved users. Understanding influence can benefit various applications …