  Graph Neural Networks (GNNs) have shown remarkable merit in performing
various learning-based tasks in complex networks. The superior performance of
GNNs often correlates with the availability and quality of node-level features
in the input networks. However, for many network applications, such node-level
information may be missing or unreliable, thereby limiting the applicability
and efficacy of GNNs. To address this limitation, we present a novel approach
denoted as Ego-centric Spectral subGraph Embedding Augmentation (ESGEA), which
aims to enhance and design node features, particularly in scenarios where
information is lacking. Our method leverages the topological structure of the
local subgraph to create topology-aware node features. The subgraph features
are generated using an efficient spectral graph embedding technique, and they
serve as node features that capture the local topological organization of the
network. The explicit node features, if present, are then enhanced with the
subgraph embeddings in order to improve the overall performance. ESGEA is
compatible with any GNN-based architecture and is effective even in the absence
of node features. We evaluate the proposed method in a social network graph
classification task where node attributes are unavailable, as well as in a node
classification task where node features are corrupted or even absent. The
evaluation results on seven datasets and eight baseline models indicate up to a
10% improvement in AUC and a 7% improvement in accuracy for graph and node
classification tasks, respectively.
