  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.

Social and Information Networks