Abstract
This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. This is done by applying the methodological framework of the Simplified Graph Convolutional Neural Network (SGC) to two academic publication datasets: Cora and Citeseer. The performance of SGC is compared to the original Graph Convolutional Network (GCN) framework. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of SGC. When removal is based on a more localized selection of nodes, augmenting the network with both strong-ties and weak-ties provides a benefit, indicating that SGC successfully leverages local information of network nodes.
Highlights
In addition to providing entertainment and social engagement, social networks serve the important function of rapidly disseminating scientific information to the research community
This section explores how the class label prediction accuracy is affected by different removal strategies when the connectivity matrix contains both the links for the strong ties and the weak ties
These results show how the parameter k can affect the accuracy of the prediction of class labels
Summary
In addition to providing entertainment and social engagement, social networks serve the important function of rapidly disseminating scientific information to the research community. Social media platforms such as ResearchGate and Academia.edu help authors rapidly find related work and supplement standard library searches. Twitter serves as an important purveyor of standard news [1] and disseminates specialty news in fields such as neuroradiology [2]. Venerable academic societies such as the Royal Society (@royalsociety) have official Twitter accounts. Given that scientific articles possess the potential to change the landscape of technology, it is important to understand the information transference properties of academic networks: can techniques originally developed for social networks yield insights about scientific networks as well?
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