Predicting the Evolution of Hot Topics: A Solution Based on the Online Opinion Dynamics Model in Social Network

Jiang, Lei; Liu, Jujun; Zhou, Dong; Zhou, Qian; Yang, Xiansheng; Yu, Guo

doi:10.1109/tsmc.2018.2876235

Cited by 24 publications

(9 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…needs to be zero. 1 Thus, at least one topic does not need to reach a consensus. (Only if ) Without loss of generality, let us suppose that there is no couple between the topics p = d − 1 and p = d; but there are couplings between all other remaining topics.…”

Section: B General Casesmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative opinion dynamics on multiple interdependent topics: Modeling and analysis

Ahn,

Van Tran,

Trinh

et al. 2018

Preprint

View full text Add to dashboard Cite

To model the interdependent couplings of multiple topics, we develop a set of rules for opinion updates of a group of agents. The rules are used to design or assign values to the elements of interdependent weighting matrices. The cooperative and anti-cooperative couplings are modeled in both the inverse-proportional and proportional feedbacks. The behaviors of cooperative opinion dynamics are analyzed using a null space property of state-dependent matrix-weighted Laplacian matrices and a Lyapunov candidate. Various consensus properties of statedependent matrix-weighted Laplacian matrices are predicted according to the intra-agent network topology and interdependency topical coupling topologies.

show abstract

Section: B General Casesmentioning

confidence: 99%

“…The problem of opinion dynamics has attracted a lot of attention recently due to its applications to decision-making processes and evolution of public opinions [1]. The opinion dynamics arises between persons who interact with each other to influence others' opinions or to update his or her opinion [2].…”

Section: Introductionmentioning

confidence: 99%

Cooperative opinion dynamics on multiple interdependent topics: Modeling and analysis

Ahn,

Van Tran,

Trinh

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…This diffusion process is essential for viral marketing, personalized recommendation, and target advertisement [5]. Therefore, attention is increasing on how influence propagates in online social networks [6]- [8]. For example, in viral marketing, the effective dissemination of the information about a product is achieved by finding a group of influential people and persuading them to promote it.…”

Section: Introductionmentioning

confidence: 99%

“…Several works aim at quantifying the diffusion of information in social networks [6], [16]- [19]. In particular, considerable attention is focused on determining how many nodes are active at the end of the diffusion process.…”

Section: Introductionmentioning

confidence: 99%

On Fast and Effective Influence Spread Maximization in Social Networks

Scarabaggio¹,

Carli²,

Dotoli³

2022

Preprint

View full text Add to dashboard Cite

The ability of social networks to disseminate information across individuals and groups is based on the social influence that people have on each other. In this context, the so-called influence maximization problem consists in identifying the most influential nodes of a given social network. This problem has practical relevance in a wide range of applications and despite the underlying computational complexity, several solution techniques have been presented in the related literature. Nonetheless, bringing together technical feasibility and satisfactory accuracy is still a challenging open research issue. In this work, we address the influence maximization problem with two complementary contributions. First, we analyze two well-known influence diffusion models, namely the independent cascade and the linear threshold models, and provide a new methodology for addressing the problem of computing the influence spread in a given network. Subsequently, we propose a novel approach to select an initial set of nodes that optimizes the influence spread in large-scale scenarios. We apply these techniques to several large-scale experiments in real-world scenarios achieving results comparable with the best-performing state-of-the-art algorithms in a shorter computational time. <h3>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</h3>

show abstract

“…MSNs can provide people with a variety of real-time information services, and have already penetrated into our daily life. The Internet-based MSNs have exhibited their great charm and broad prospect in many application fields, such as instant communication, life service, interactive entertainment, etc., and have attracted extensive attention of the industry and the academia [2], [3]. However, the development of MSNs is like a double-edged sword [4], [5].…”

Section: Introductionmentioning

confidence: 99%