Influential Nodes Detection in Dynamic Social Networks

Hafiene, Nesrine; Karoui, Wafa; Romdhane, Lotfi

doi:10.1007/978-3-030-20482-2_6

Cited by 7 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There has been a disciplinary widespread search, for various purposes-social, technological, and epidemiological-for principles governing most influential nodes within a network (Kempe, Kleinberg, and Tardos 2005;Chen et al 2013;Bao et al 2017;Li, Zhang, and Deng 2018;Champion and Elkan 2017;Wei et al 2018;Hafiene, Karoui, and Romdhane 2019). Essentially all of this work, however, has been on networks far simpler than the Bayesian models we envisage here-on undirected networks without weighted links, for example.…”

Section: How Evidence Work: Interactive Factors In Scientific Networkmentioning

confidence: 99%

“…In section 6 the treatment is expanded to directed acyclic graphs in general. The search for generalizations regarding influential nodes in networks of various sorts is extensive across disciplines, incomplete and ongoing (Kempe, Kleinberg, and Tardos 2005;Chen et al 2013;Lawyer 2015;Bao et al 2017;Li, Zhang, and Deng 2018;Champion and Elkan 2017;Wei et al 2018;Hafiene, Karoui, and Romdhane 2019). Ours is a particular form of that search, geared to a particular kind of evidence influence in theoretical credence networks in particular.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity

et al. 2022

View full text Add to dashboard Cite

We model scientific theories as Bayesian networks. Nodes carry credences and function as abstract representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to envisage and analyze the differential impact of evidence and credence change at different points within a single network and across different theoretical structures.

show abstract

Section: How Evidence Work: Interactive Factors In Scientific Networkmentioning

confidence: 99%

mentioning

confidence: 99%

Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity

et al. 2022

View full text Add to dashboard Cite

show abstract

Parallel social behavior-based algorithm for identification of influential users in social network

2021

View full text Add to dashboard Cite

Influence maximization in social networks refers to the process of finding influential users who make the most of information or product adoption. The social networks is prone to grow exponentially, which makes it difficult to analyze. Critically, most of approaches in the literature focus only on modeling structural properties, ignoring the social behavior in the relations between users. For this, we tend to parallelize the influence maximization task based on social behavior. In this paper, we introduce a new parallel algorithm, named PSAIIM, for identification of influential users in social network. In PSAIIM, we uses two semantic metrics: the user’s interests and the dynamically-weighted social actions as user interactive behaviors. In order to overcome the size of actual real-world social networks and to minimize the execution time, we used the community structure to apply perfect parallelism to the CPU architecture of the machines to compute an optimal set of influential nodes. Experimental results on real-world networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms, especially in the speed of calculation.

show abstract