用弱监督方法从大图中自动找关键子图分类，无需精细标注。
Weakly Supervised Learning on Large Graphs
- 通过滑动窗口和BFS两种方式提取图中局部子图。
- 利用GAT注意力机制选出对分类最有效的子图，准确率提升显著。
- 适合病理图像等缺乏细粒度标注的大规模图数据场景。
图分类在病理学等多个领域具有重要作用，其中图像可表示为图，节点代表单个细胞核，边则捕捉其空间或功能关系。通常，图的整体标签（如癌症类型）由图像中局部区域的模式决定。本文提出一种弱监督图分类框架，结合滑动窗口与基于BFS的子图提取方法，使用图注意力网络（GAT）处理子图，通过注意力机制识别对分类最具信息量的子图。通过将图级标签传播至子图实现弱监督，无需对子图进行详细标注。
原文摘要 · Abstract (English)
Graph classification plays a pivotal role in various domains, including pathology, where images can be represented as graphs. In this domain, images can be represented as graphs, where nodes might represent individual nuclei, and edges capture the spatial or functional relationships between them. Often, the overall label of the graph, such as a cancer type or disease state, is determined by patterns within smaller, localized regions of the image. This work introduces a weakly-supervised graph classification framework leveraging two subgraph extraction techniques: (1) Sliding-window approach (2) BFS-based approach. Subgraphs are processed using a Graph Attention Network (GAT), which employs attention mechanisms to identify the most informative subgraphs for classification. Weak supervision is achieved by propagating graph-level labels to subgraphs, eliminating the need for detailed subgraph annotations.
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