Graph neural networks (GNNs) have exhibited prominent performance in learning
graph-structured data. Considering node classification task, based on the i.i.d
assumption among node labels, the traditional supervised learning simply sums
up cross-entropy losses of the independent training nodes and applies the
average loss to optimize GNNs' weights. But different from other data formats,
the nodes are naturally connected. It is found that the independent
distribution modeling of node labels restricts GNNs' capability to generalize
over the entire graph and defend adversarial attacks. In this work, we propose
a new framework, termed joint-cluster supervised learning, to model the joint
distribution of each node with its corresponding cluster. We learn the joint
distribution of node and cluster labels conditioned on their representations,
and train GNNs with the obtained joint loss. In this way, the data-label
reference signals extracted from the local cluster explicitly strengthen the
discrimination ability on the target node. The extensive experiments
demonstrate that our joint-cluster supervised learning can effectively bolster
GNNs' node classification accuracy. Furthermore, being benefited from the
reference signals which may be free from spiteful interference, our learning
paradigm significantly protects the node classification from being affected by
the adversarial attack.

本文提出了一种新的框架，称为联合簇监督学习，用于建模每个节点与其相应簇的联合分布，通过联合损失训练图神经网络，从而明确地增强目标节点的区分能力，并且有效地保护节点分类不受敌对攻击的影响。