HeterGCL 论文写作分析

这篇文章，由于理论证明较少，因此写作风格了polygcl是两种风格的。polygcl偏向理论的写作风格，而hetergcl就是实践派的风格

首先看标题，其的重点是Graph contrastive learning Framework。其重点是framework，以及heterophy

摘要

第一句：设定背景图对比学习效果好

图对比学习（GCL）因其学习稳健节点表示的自我监督能力而引起了研究的广泛关注。

Graph Contrastive Learning (GCL) has attracted significant research attention due to its selfsupervised ability to learn robust node representations.

语法学习：due to A to do be。可以用到（防止千篇一律)

第二句：提出问题图对比学习在异配图上匮乏

不幸的是，大多数方法主要集中在同嗜图上，这使得它们对异嗜图的效果较差。此外，由于问题背景的不同，因此对比学习的增强方案等需要重新设计

Unfortunately, most methods primarily focus on homophilic graphs, rendering them less effective for heterophilic graphs. In addition, the complexity of node interactions in heterophilic graphs poses considerable challenges to augmentation schemes, coding architectures, and contrastive designs for traditional GCL.

语法学习：render，导致。considerable challenges，相当多的挑战

第三步：引出解决方案引入结构和语义信息的新型对比学习框架

In this work, we propose HeterGCL, a novel graph contrastive learning framework with structural and semantic learning to explore the true potential of GCL on heterophilic graphs.

在这项工作中，我们提出了HeterGCL，这是一个具有结构和语义学习的新型图对比学习框架，以探索GCL在异性图上的真正潜力。

第四步：具体的做法

Specifically, We abandon the random augmentation scheme that leads to the destruction of the graph structure, instead introduce an adaptive neighbor aggregation strategy (ANA) to extract topology-supervised signals from neighboring nodes at different distances and explore the structural information with an adaptive local-to-global contrastive loss. In the semantic learning module, we jointly consider the original nodes’ features and the similarity between nodes in the latent feature space to explore hidden associations between nodes.

介绍了两种结构和语义的具体做法