In recent years, graph neural networks (GNNs) have emerged as a potent tool
for learning on graph-structured data and won fruitful successes in varied
fields. The majority of GNNs follow the message-passing paradigm, where
representations of each node are learned by recursively aggregating features of
its neighbors. However, this mechanism brings severe over-smoothing and
efficiency issues over high-degree graphs (HDGs), wherein most nodes have
dozens (or even hundreds) of neighbors, such as social networks, transaction
graphs, power grids, etc. Additionally, such graphs usually encompass rich and
complex structure semantics, which are hard to capture merely by feature
aggregations in GNNs. Motivated by the above limitations, we propose TADA, an
efficient and effective front-mounted data augmentation framework for GNNs on
HDGs. Under the hood, TADA includes two key modules: (i) feature expansion with
structure embeddings, and (ii) topology- and attribute-aware graph
sparsification. The former obtains augmented node features and enhanced model
capacity by encoding the graph structure into high-quality structure embeddings
with our highly-efficient sketching method. Further, by exploiting
task-relevant features extracted from graph structures and attributes, the
second module enables the accurate identification and reduction of numerous
redundant/noisy edges from the input graph, thereby alleviating over-smoothing
and facilitating faster feature aggregations over HDGs. Empirically, TADA
considerably improves the predictive performance of mainstream GNN models on 8
real homophilic/heterophilic HDGs in terms of node classification, while
achieving efficient training and inference processes.

TADA 是一种前置数据增强框架，用于高度图中的图神经网络，通过结构嵌入和拓扑属性感知图稀疏化的方式，提高了主流 GNN 模型在节点分类任务上的预测性能。