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Accurate graph classification via two-staged contrastive curriculum learning.

Sooyeon Shim¹, Junghun Kim¹, Kahyun Park¹

¹Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.

|January 3, 2024

Summary

This summary is machine-generated.

This study introduces TAG, a novel graph contrastive learning method. TAG enhances graph representations for better classification accuracy by considering both nodes and graphs.

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Area of Science:

Artificial Intelligence
Machine Learning
Graph Representation Learning

Background:

Learning effective graph embeddings is crucial for graph-based tasks.
Existing graph contrastive learning methods lack semantic information and dual-level considerations (nodes and graphs).

Purpose of the Study:

To propose TAG (Two-staged contrAstive curriculum learning for Graphs), a novel method for learning graph representations.
To improve graph classification accuracy by addressing limitations in current contrastive learning approaches.

Main Methods:

TAG employs a two-staged contrastive learning approach at both node and graph levels.
It utilizes six degree-based, model-agnostic augmentation algorithms for representation learning.

Main Results:

TAG significantly outperforms existing unsupervised and supervised methods in graph classification accuracy.
Achieved average improvements of up to 4.08% and 4.76% over the second-best methods, respectively.

Conclusions:

TAG offers a superior approach to learning graph representations for classification tasks.
The method effectively captures semantic information and integrates node and graph-level learning.