Graph data widely exists in real life, with large amounts of data and complex
structures. It is necessary to map graph data to low-dimensional embedding.
Graph classification, a critical graph task, mainly relies on identifying the
important substructures within the graph. At present, some graph classification
methods do not combine the multi-granularity characteristics of graph data.
This lack of granularity distinction in modeling leads to a conflation of key
information and false correlations within the model. So, achieving the desired
goal of a credible and interpretable model becomes challenging. This paper
proposes a causal disentangled multi-granularity graph representation learning
method (CDM-GNN) to solve this challenge. The CDM-GNN model disentangles the
important substructures and bias parts within the graph from a
multi-granularity perspective. The disentanglement of the CDM-GNN model reveals
important and bias parts, forming the foundation for its classification task,
specifically, model interpretations. The CDM-GNN model exhibits strong
classification performance and generates explanatory outcomes aligning with
human cognitive patterns. In order to verify the effectiveness of the model,
this paper compares the three real-world datasets MUTAG, PTC, and IMDM-M. Six
state-of-the-art models, namely GCN, GAT, Top-k, ASAPool, SUGAR, and SAT are
employed for comparison purposes. Additionally, a qualitative analysis of the
interpretation results is conducted.

该研究论文提出了一种因果解缠多粒度图表示学习方法（CDM-GNN），用于解决图数据建模中多粒度特征的融合问题，该方法在图分类任务中表现出强大的性能，并生成与人类认知模式相一致的解释结果。