Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network

Cheng, Kefei; Guo, Xiaoyong; Cui, Xiaotong; Shan, Fengchi

doi:10.1155/2020/3273451

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Some recent works have focused on other aspects of different types of SLNs, e.g., MOOCs [12], [21], [35], Q&A sites [22], [36], and enterprise social networks [37], [38]. Our work is perhaps most similar to [2], [21] in that we study prediction for SLNs using topological features.…”

Section: A Related Workmentioning

confidence: 97%

“…We use y uv (i) as an indicator variable for the formation of link (u, v): y uv (i) = 1 if a link between u and v has been created in any interval up to and including i, and y uv (i) = 0 otherwise. Thus, as in most social networks [38] [16], links persist over time in our SLN model. The SLN graph structure in any given interval i is then comprised of nodes corresponding to the learners u and edges (u, v) corresponding to links between them.…”

Section: A Sln Graph Modelmentioning

confidence: 99%

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Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

Sahay

Zhang

Yang

et al. 2023

IEEE/ACM Trans. Networking

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We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured interactions. While link prediction has been studied for general types of social networks, the evolution of SLNs over their lifetimes coupled with their dependence on which topics are being discussed presents new challenges for this type of network. To address these challenges, we develop a series of autonomous link prediction methodologies that utilize spatial and time-evolving network architectures to pass network state between space and time periods, and that models over three types of SLN features updated in each period: neighborhoodbased (e.g., resource allocation), path-based (e.g., shortest path), and post-based (e.g., topic similarity). Through evaluation on six real-world datasets from Massive Open Online Course (MOOC) discussion forums and from Purdue University, we find that our method obtains substantial improvements over Bayesian models, linear classifiers, and graph neural networks, with AUCs typically above 0.91 and reaching 0.99 depending on the dataset. Our feature importance analysis shows that while neighborhood and path-based features contribute the most to the results, post-based features add additional information that may not always be relevant for link prediction.

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Section: A Related Workmentioning

confidence: 97%

Section: A Sln Graph Modelmentioning

confidence: 99%

Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

Sahay

Zhang

Yang

et al. 2023

IEEE/ACM Trans. Networking

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“…e evaluation and prediction method of deep learning is easy to implement and will not disclose any personal information. Learners prefer to be able to use similarity relations for analysis [14] so as to improve the recommendation accuracy. With the continuous growth of the amount, complexity, and dynamics of online information [15], the recommendation system provides personalized recommendation by retrieving the most relevant information and services from a large amount of data.…”

Section: Introductionmentioning

confidence: 99%

Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

Wang

2022

Scientific Programming

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With the popularization of online education and the idea of learning anytime and anywhere, more and more learners search and learn courses of various disciplines through online learning platforms to meet their personal knowledge needs. With the increase of the number of courses, it is difficult for learners to find the courses they want quickly and accurately; that is, they encounter the problems of information overload and cognitive maze. Therefore, how to recommend personalized courses for learners according to their preferences has become one of the important problems that need to be solved urgently to improve the service quality of courses in online learning platforms. Therefore, in order to improve the accuracy of course recommendation, it is necessary to build an accurate and complete learner model. In order to improve the application effect of recommendation, this paper focuses on the recommendation method of emotional factors to improve the recommendation efficiency of learning resources. The traditional recommendation model is a method based on the user’s purchase behavior and historical information. However, in the emotional factors, the effect of traditional recommendation is limited. This paper proposes a recommendation method based on emotional factors, which may consider the emotional and psychological factors of scholars according to the learning content of learners. The experimental results show that the learner model incorporating learners’ affection can reflect learners’ preferences more accurately, and the use of deep neural factorization machine for curriculum recommendation can effectively improve the accuracy of curriculum recommendation.

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A Realistic Criterion for Team Formation in Social Network

Samie

Rajabzadeh²

2022

Iran J Sci Technol Trans Electr Eng

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Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network

Cited by 4 publications

References 11 publications

Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

A Realistic Criterion for Team Formation in Social Network

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