Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph

Tran, Quan M.; Nguyen, Hien D.; Huynh, Tai; Nguyen, Kha V.; Hoang, Suong N.; Pham, Vuong T.

doi:10.1007/s10878-021-00815-0

Cited by 9 publications

(3 citation statements)

References 42 publications

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“…It can learn user attributes and user relationships. The model is robust to unlabeled data [ 37 , 38 ]. Moreover, in each layer of the neural network, many perceptron serve as the load-bearing nodes.…”

Section: Related Workmentioning

confidence: 99%

Detecting influential nodes with topological structure via Graph Neural Network approach in social networks

Bhattacharya

Nagwani

Tripathi

2023

Int. j. inf. tecnol.

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Detecting influential nodes in complex social networks is crucial due to the enormous amount of data and the constantly changing behavior of existing topologies. Centrality-based and machine-learning approaches focus mostly on node topologies or feature values in their evaluation of nodes’ relevance. However, both network topologies and node attributes should be taken into account when determining the influential value of nodes. This research has proposed a deep learning model called Graph Convolutional Networks (GCN) to discover the significant nodes in graph-based large datasets. A deep learning framework for identifying influential nodes with structural centrality via Graph Convolutional Networks called DeepInfNode has been developed. The proposed approach measures up contextual information from Susceptible-Infected-Recovered (SIR) model trials to measure the rate of infection to develop node representations. In the experimental section, acquired experimental results indicate that the suggested model has a higher F1 and Area under the curve (AUC) value. The findings indicate that the strategy is both effective and precise in terms of suggesting new linkages. The proposed DeepInfNode model outperforms state-of-the-art approaches on a variety of publicly available standard graph datasets, achieving an increase in performance of up to 99.1% of accuracy.

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Section: Related Workmentioning

confidence: 99%

Detecting influential nodes with topological structure via Graph Neural Network approach in social networks

Bhattacharya

Nagwani

Tripathi

2023

Int. j. inf. tecnol.

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“…For users on a social network, the measuring of their impact on that network has been studied by many methods [ 22 , 28 ], such as: using association rules [ 29 ], nomological network [ 30 ], diffusion model [ 31 ]. Those methods can be classified as Local Measures [ 27 , 32 ], Short Path–Based Measures [ 33 ], Iterative Calculation–Based Measures [ 34 , 35 ], Coreness-Based Measures [ 36 ], and Machine-Learning Algorithms [ 37 , 38 ].…”

Section: Related Workmentioning

confidence: 99%

A method to detect influencers in social networks based on the combination of amplification factors and content creation

Huynh

Nguyen²,

Zelinka³

et al. 2022

PLoS ONE

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A social network is one of the efficient tools for information propagation. The content is the bridge between the product and its customers. Evaluating the user’s content creation is a valuable feature to improve information spreading on the social network. This paper proposes a method for extracting brand value with influencers by combining the user’s amplification and content creation in influencer marketing. The amplification factors are studied based on the propagation of the posts on the social network in a duration time. Those factors are more valuable than before when using influencer marketing at a determined time. Moreover, the content creation score is also studied to measure content creation based on the passion point with a brand and its quality. The amplification factors and content creation score are combined to analyze posts’ interest in detecting the emerging influent users for a product in the influencer marketing campaign. Using the amplification factors, the passion points, and the content creation score, a system to manage the influencer marketing on Facebook has been constructed and tested in the real-world campaign. The experimental results show that the proposed method’s influencers bring the conversion rate’s efficiency and revenue in the influencer marketing campaign.

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“…The knowledge graph is a knowledge base that integrates data using a graph-structured model (Tran et al, 2022;. This approach is used to store interconnected descriptions of entities that are objects and abstract concepts.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Management for Information Querying System in Education via the Combination of Rela-Ops Model and Knowledge Graph

Nguyen

Truong

et al. 2023

Journal of Cases on Information Technology

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E-learning is an online educational system that uses electronic and technology tools and the internet. To facilitate the acquisition of knowledge, students can consult materials and interact directly with lecturers via e-learning educational systems. The method for developing an intelligent querying system for e-learning is suggested in this research. The knowledge base of the system is structured using the knowledge model combining an ontology and the two-layer knowledge graph, called Rela-KG model. The searching problems for knowledge content are studied and solved. The technique is applied to create an intelligent querying system on the course of Database Foundation in Information Technology (IT) at the university. This technique attempts to assist students in reviewing lectures and better comprehending course material through independent study. The experimental results indicate that it would be anticipated to support student access to online learning resources.

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Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph

Cited by 9 publications

References 42 publications

Detecting influential nodes with topological structure via Graph Neural Network approach in social networks

Detecting influential nodes with topological structure via Graph Neural Network approach in social networks

A method to detect influencers in social networks based on the combination of amplification factors and content creation

Knowledge Management for Information Querying System in Education via the Combination of Rela-Ops Model and Knowledge Graph

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