Unsupervised/self-supervised graph representation learning is critical for downstream node- and graph-level classification tasks. Global structure of graphs helps discriminating representations and existing methods mainly utilize the global structure by imposing additional supervisions. However, their global semantics are usually invariant for all nodes/graphs and they fail to explicitly embed the global semantics to enrich the representations. In this paper, we propose Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning (OEPG). Specifically, we introduce instance-adaptive global-aware ego-semantic descriptors, leveraging the first- and second-order feature differences between each node/graph and hierarchical global clusters of the entire graph dataset. The descriptors can be explicitly integrated into local graph convolution as new neighbor nodes. Besides, we design an omni-granular normalization on the whole scales and hierarchies of the ego-semantic to assign attentional weight to each descriptor from an omni-granular perspective. Specialized pretext tasks and cross-iteration momentum update are further developed for local-global mutual adaptation. In downstream tasks, OEPG consistently achieves the best performance with a 2%~6% accuracy gain on multiple datasets cross scales and domains. Notably, OEPG also generalizes to quantity- and topology-imbalance scenarios.
翻译:在下游节点和图层层次的分类任务中,不受监督/自我监督的图形代表学习至关重要。全球图表结构有助于区别对待的表达形式和现有方法,主要通过施加更多的监督来利用全球结构。然而,其全球语义对于所有节点/图谱通常都是变化不定的,它们没有明确地将全球语义嵌入全球语义以丰富表述内容。在本文中,我们提议为自我强化的图表代表情景学习设计Omni-Granulal Ego-Semantical Provision化(OEPG)。具体地说,我们引入了能适应的全局性全球自觉描述描述描述和现有方法的描述性标本,利用第一和第二顺序的表达方式在全部图形数据集的每个节点/图组和等级全球组之间有差异。解语义可以作为新的相邻节点被明确融入本地的图解演进。此外,我们设计了全局和自我强化的图像表达式结构,从omni-granal-me-meal-meal-destrual imalal imal imal ladeal ladeal ladeal ladeal dal dal dald ex ex ex lax lax lax lax 2 lax lax lax lax