  Graph neural networks (GNNs), in general, are built on the assumption of a
static set of features characterizing each node in a graph. This assumption is
often violated in practice. Existing methods partly address this issue through
feature imputation. However, these techniques (i) assume uniformity of feature
set across nodes, (ii) are transductive by nature, and (iii) fail to work when
features are added or removed over time. In this work, we address these
limitations through a novel GNN framework called GRAFENNE. GRAFENNE performs a
novel allotropic transformation on the original graph, wherein the nodes and
features are decoupled through a bipartite encoding. Through a carefully chosen
message passing framework on the allotropic transformation, we make the model
parameter size independent of the number of features and thereby inductive to
both unseen nodes and features. We prove that GRAFENNE is at least as
expressive as any of the existing message-passing GNNs in terms of
Weisfeiler-Leman tests, and therefore, the additional inductivity to unseen
features does not come at the cost of expressivity. In addition, as
demonstrated over four real-world graphs, GRAFENNE empowers the underlying GNN
with high empirical efficacy and the ability to learn in continual fashion over
streaming feature sets.
