Recovering Missing Node Features with Local Structure-based Embeddings
This work addresses a common issue in graph-based learning for researchers and practitioners, though it is incremental as it builds on existing methods like Graph AutoEncoders.
The paper tackles the problem of missing node features in graph data by developing a framework that recovers these features using local structure-based embeddings, achieving accurate feature estimation and improving downstream graph classification performance.
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.