Node features bolster graph-based learning when exploited jointly with
network structure. However, a lack of nodal attributes is prevalent in graph
data. We present a framework to recover completely missing node features for a
set of graphs, where we only know the signals of a subset of graphs. Our
approach incorporates prior information from both graph topology and existing
nodal values. We demonstrate an example implementation of our framework where
we assume that node features depend on local graph structure. Missing nodal
values are estimated by aggregating known features from the most similar nodes.
Similarity is measured through a node embedding space that preserves local
topological features, which we train using a Graph AutoEncoder. We empirically
show not only the accuracy of our feature estimation approach but also its
value for downstream graph classification. Our success embarks on and implies
the need to emphasize the relationship between node features and graph
structure in graph-based learning.

通过结合网络结构，我们提出了一种从一组图中完全恢复缺失节点特征的框架，该框架利用了先前的拓扑和节点特征信息，并通过聚合相似节点的已知特征来估计缺失节点值，在图基学习中强调了节点特征和图结构的关系。