  Graph Neural Networks (GNNs) are gaining increasing attention on graph data
learning tasks in recent years. However, in many applications, graph may be
coming in an incomplete form where attributes of graph nodes are partially
unknown/missing. Existing GNNs are generally designed on complete graphs which
can not deal with attribute-incomplete graph data directly. To address this
problem, we develop a novel partial aggregation based GNNs, named Partial Graph
Neural Networks (PaGNNs), for attribute-incomplete graph representation and
learning. Our work is motivated by the observation that the neighborhood
aggregation function in standard GNNs can be equivalently viewed as the
neighborhood reconstruction formulation. Based on it, we define two novel
partial aggregation (reconstruction) functions on incomplete graph and derive
PaGNNs for incomplete graph data learning. Extensive experiments on several
datasets demonstrate the effectiveness and efficiency of the proposed PaGNNs.
