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.

在复杂网络中，图神经网络（GNNs）在各种基于学习的任务中表现出显著的优势。然而，对于许多网络应用而言，节点级信息可能缺失或不可靠，从而限制了 GNNs 的适用性和效果。为了解决这个问题，我们提出了一种名为 Ego-centric Spectral subGraph Embedding Augmentation（ESGEA）的新方法，旨在增强和设计节点特征，特别是在信息缺乏的情况下。我们的方法利用局部子图的拓扑结构来创建具有拓扑感知能力的节点特征。子图特征通过高效的谱图嵌入技术生成，并作为捕捉网络局部拓扑结构的节点特征。如果存在显式节点特征，则使用子图嵌入来增强这些特征以提高整体性能。ESGEA 适用于任何基于 GNN 的架构，即使在没有节点特征的情况下也能有效。我们在一个社交网络图分类任务以及一个节点分类任务上对所提出的方法进行了评估，其中节点属性不可用或特征被破坏甚至不存在。对七个数据集和八个基准模型的评估结果表明，在图分类任务和节点分类任务中，AUC 值和准确度分别提高了 10% 和 7%。