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

Rajeev Sahay, Serena Nicoll, Minjun Zhang, Tsung Yen Yang, Carlee Joe-Wong, Kerrie A. Douglas, Christopher G. Brinton

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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: neighborhood-based (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. The code and four of the datasets used in this work are available at https://github.com/Jess-jpg-txt/sln-learning.

Original languageEnglish (US)
Pages (from-to)2086-2100
Number of pages15
JournalIEEE/ACM Transactions on Networking
Volume31
Issue number5
DOIs
StatePublished - Oct 1 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • Deep learning
  • graph neural networks
  • link prediction
  • online social networks
  • social learning networks

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