You do not have to train Graph Neural Networks at all on text-attributed
  graphs
Kaiwen Dong Zhichun Guo Nitesh V. Chawla
Graph structured data, specifically text-attributed graphs (TAG), effectively
represent relationships among varied entities. Such graphs are essential for
semi-supervised node classification tasks. Graph Neural Networks (GNNs) have
emerged as a powerful tool for handling this graph-structured data. Although
gradient descent is commonly utilized for training GNNs for node
classification, this study ventures into alternative methods, eliminating the
iterative optimization processes. We introduce TrainlessGNN, a linear GNN model
capitalizing on the observation that text encodings from the same class often
cluster together in a linear subspace. This model constructs a weight matrix to
represent each class's node attribute subspace, offering an efficient approach
to semi-supervised node classification on TAG. Extensive experiments reveal
that our trainless models can either match or even surpass their conventionally
trained counterparts, demonstrating the possibility of refraining from gradient
descent in certain configurations.