A Knowledge Diffusion Model in Autonomous Learning Under Multiple Networks for Personalized Educational Resource Allocation

Wan, Pengfei; Wang, Xiaoming; Lin, Yaguang; Pang, Guangyao

doi:10.1109/tlt.2021.3103006

Cited by 11 publications

(2 citation statements)

References 47 publications

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“…Then a recurrent neural network model with graph attention mechanism, which constructs a seq2seq framework to learn the spatial-temporal cascade features, was built to learn their structural context in several different time intervals based on timestamp with a time-decay attention and predict the next user with the latest cascade representation [23]. A formal model was developed to characterize the dynamic process of knowledge diffusion in the autonomous learning under multiple networks to expand the scope of knowledge through hybrid online learning for allocating educational resources [24]. Neural Diffusion Model (NDM) made relaxed assumptions and employs deep learning techniques including attention mechanism and convolutional network for better fitting the diffusion data and generalize to unseen cascades [25].…”

Section: Stochastic Modeling Approachmentioning

confidence: 99%

An Online Topic Diffusion Prediction Approach on Heterogeneous Social Networks

2023

JRAR

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Online topic indicates the diffusing process of various attitudes and emotions towards some events expressing and propagating over the Internet. It is an essential for monitoring all online information and ensuring internet information safety to search for the predicting approach of the trend of online topic. Previous methods focus on special social network and lack of versatility because of intensely relying on manual experience, that we are devoted to conquering these shortages. In the manuscript, we divide online topic into unimodal topic and multimodal topic which is the combination of a series of unimodal topics. We present a generally nonlinear dynamic model to describe unimodal topic diffusion mechanism on heterogeneous social network. The model can capture the long-term development tendency of unimodal topic, So, we present a long-term tendency prediction method of unimodal online topic with help of the dynamic model. But the model is not for multimodal online topic in that their long-term development tendency is made up of a series of unimodal topics’ long-term development tendency, which leads to the unpredictability of their long-term future directions, we propose a short-term population prediction method of unimodal and multimodal online topic diffusion on heterogeneous social network based on the essential relationship between unimodal and multimodal topic. Experimental results are our two methods have good performance on predicting the long-term development tendency of unimodal topic and all topic’s short-term population.

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Section: Stochastic Modeling Approachmentioning

confidence: 99%

An Online Topic Diffusion Prediction Approach on Heterogeneous Social Networks

2023

JRAR

View full text Add to dashboard Cite

show abstract

“…Then a recurrent neural network model [23] with graph attention mechanism, which constructs a seq2seq framework to learn the spatial-temporal cascade features, was built to learn their structural context in several different time intervals based on timestamp with a time-decay attention and predict the next user with the latest cascade representation. A formal model [24] was developed to characterize the dynamic process of knowledge diffusion in the autonomous learning under multiple networks to expand the scope of knowledge through hybrid online learning for allocating educational resources. Neural Diffusion Model (NDM) [25] made relaxed assumptions and employs deep learning techniques including attention mechanism and convolutional network for better fitting the diffusion data and generalize to unseen cascades.…”

Section: Data Mining-based Approachmentioning

confidence: 99%

an Online Topic Diffusion Prediction Approach on Heterogeneous Social Networks

Zhang

et al. 2022

Preprint

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Online Topic indicates the diffusing process of various attitudes and emotions towards some events expressing and propagating over the Internet. It is an essential for monitoring all online information and ensuring internet information safety to search for the predicting approach of the trend of online topic. Previous methods focus on special social network and lack of versatility because of intensely relying on manual experience, that we are devoted to conquering these shortages. In the manuscript, we divide online topic into unimodal topic and multimodal topic, and multimodal topic is the combination of a series of unimodal topics. We present a generally nonlinear dynamic model to give a description of unimodal online topic diffusion mechanism on heterogeneous social network. The model can capture the long-term development tendency of unimodal topic, So, we present a long-term tendency prediction method of unimodal online topic with help of the dynamic model.But the model is not for multimodal online topic in that their long-term development tendency is made up of a series of unimodal topics’ long-term development tendency, which leads to the unpredictability of their long-term future directions, we propose a short-term population prediction method of unimodal and multimodal online topic diffusion on heterogeneous social network based on the essential relationship between unimodal and multimodal topic. Experimental results are our two methods have good performance on predicting the long-term development tendency of unimodal topic and all topic’s short-term population.

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Balanced Allocation of Ideological and Political Network Teaching Resources in Universities Based on Big Data Clustering

Chen

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Knowledge Diffusion Model in Autonomous Learning Under Multiple Networks for Personalized Educational Resource Allocation

Cited by 11 publications

References 47 publications

An Online Topic Diffusion Prediction Approach on Heterogeneous Social Networks

An Online Topic Diffusion Prediction Approach on Heterogeneous Social Networks

an Online Topic Diffusion Prediction Approach on Heterogeneous Social Networks

Balanced Allocation of Ideological and Political Network Teaching Resources in Universities Based on Big Data Clustering

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