论文标题
TAM：类不平衡节点分类的拓扑感知的利润率损失
TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification
论文作者
论文摘要
由于相邻节点之间的相互作用，在类不平衡的图形数据下学习无偏的节点表示具有挑战性。现有的研究共同认为，它们根据其总数（忽略图中的节点连接）来补偿次要类节点“作为组”，这不可避免地增加了主要节点的假阳性病例。我们假设这些假阳性病例的增加受每个节点周围的标签分布的高度影响，并通过实验确认。此外，为了解决这个问题，我们提出拓扑意识的利润率（TAM），以反映学习目标的本地拓扑。我们的方法将每个节点的连接模式与类平均反向零件进行比较，并根据此相应地适应边缘。我们的方法始终在具有代表性GNN体系结构的各种节点分类基准数据集上表现出优于基线的优势。
Learning unbiased node representations under class-imbalanced graph data is challenging due to interactions between adjacent nodes. Existing studies have in common that they compensate the minor class nodes `as a group' according to their overall quantity (ignoring node connections in graph), which inevitably increase the false positive cases for major nodes. We hypothesize that the increase in these false positive cases is highly affected by the label distribution around each node and confirm it experimentally. In addition, in order to handle this issue, we propose Topology-Aware Margin (TAM) to reflect local topology on the learning objective. Our method compares the connectivity pattern of each node with the class-averaged counter-part and adaptively adjusts the margin accordingly based on that. Our method consistently exhibits superiority over the baselines on various node classification benchmark datasets with representative GNN architectures.
