Introduction In the last blog we gave a brief introduction to Graph Convolution Networks and examined the model, $\textsc{TextGCN}$, which builds a graph from word occurrences in documents and utilizes GCNs to classify documents (the nodes in the constructed graph). Likewise, with COVID-19 affecting people all over the…
Introduction There have been many interesting applications of graph structure in text data for natural language processing tasks. For a brief overview check out my other article here. In this article, I want to go more in depth into one of the papers that had…
Introduction In recent years, there has been an explosion of interest in neural networks designed for graph structured data. Graph structured data is any set of nodes connected by edges. We denote a graph as $ \mathcal{G}=(V,E) $ with node set $ V $ and…