A Causal Disentangled Multi-Granularity Graph Classification Method
Yuan Li Li Liu Penggang Chen Youmin Zhang Guoyin Wang
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.