GAFGCN: A graph augmented fusion network for enhanced deep clustering in attributed graphs.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Mining

Background:

• Attributed graph clustering partitions nodes using structural and attribute similarities.
• Existing methods may not fully capture complex relationships or multi-scale features.

Purpose of the Study:

• To propose a novel Graph Augmented Fusion Clustering Network (GAFGCN) for enhanced attributed graph clustering.
• To improve node representation by integrating structural and attribute information across multiple scales.

Main Methods:

• Developed a four-module network: Information-enhanced Encoder Module (IEM), Graph Augmentation and Information Extraction Module (GAIE), Parallel Information Learning Module (PILM), and Self-Adaptive Fusion Learning Module (SFLM).
• Introduced an enhanced graph construction strategy and captured multi-scale features from local to global perspectives.
• Employed adaptive fusion of node representations for clustering.

Main Results:

• The proposed GAFGCN demonstrated superior performance compared to state-of-the-art methods.
• Experiments on nine diverse datasets validated the effectiveness of the GAFGCN framework.
• The method successfully integrated structural and attribute information for improved clustering outcomes.

Conclusions:

• GAFGCN offers an effective approach to attributed graph clustering by leveraging enhanced graph construction and multi-scale feature learning.
• The adaptive fusion mechanism contributes to robust and accurate partitioning of nodes.
• This work advances the field of graph-based machine learning and clustering.