A Causal Disentangled Multi-Granularity Graph Classification Method

• URL: http://arxiv.org/abs/2310.16256v1
• Date: Wed, 25 Oct 2023 00:20:50 GMT
• Title: A Causal Disentangled Multi-Granularity Graph Classification Method
• Authors: Yuan Li, Li Liu, Penggang Chen, Youmin Zhang, Guoyin Wang
• Abstract summary: Some graph classification methods do not combine the multi-granularity characteristics of graph data. This paper proposes a causal disentangled multi-granularity graph representation learning method (CDM-GNN) to solve this challenge. The model exhibits strong classification performance and generates explanatory outcomes aligning with human cognitive patterns.
• Score: 18.15154299104419
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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