  Graph neural networks (GNNs) have achieved superior performance on node
classification tasks in the last few years. Commonly, this is framed in a
transductive semi-supervised learning setup wherein the entire graph, including
the target nodes to be labeled, is available for training. Driven in part by
scalability, recent works have focused on the inductive case where only the
labeled portion of a graph is available for training. In this context, our
current work considers a challenging inductive setting where a set of labeled
graphs are available for training while the unlabeled target graph is
completely separate, i.e., there are no connections between labeled and
unlabeled nodes. Under the implicit assumption that the testing and training
graphs come from similar distributions, our goal is to develop a labeling
function that generalizes to unobserved connectivity structures. To that end,
we employ a graph neural tangent kernel (GNTK) that corresponds to infinitely
wide GNNs to find correspondences between nodes in different graphs based on
both the topology and the node features. We augment the capabilities of the
GNTK with residual connections and empirically illustrate its performance gains
on standard benchmarks.
