An improved multi-view attention network inspired by coupled P system for node classification.

Area of Science:

• Computer Science
• Artificial Intelligence
• Network Science

Background:

• Existing graph embedding methods primarily address single-view networks and single relations.
• Real-world networks often exhibit complex multi-view relationships, exceeding the capabilities of current methods.
• Node classification in multi-view networks remains a significant challenge.

Purpose of the Study:

• To propose a novel multi-view attention network inspired by coupled P systems (MVAN-CP) for effective node classification.
• To extract rich information from multiple network views and generate view-specific representations.
• To enhance collaboration between network views through an attention-based fusion process.

Main Methods:

• Development of a multi-view attention network to process diverse network perspectives.
• Application of an attention mechanism to intelligently fuse information from different views.
• Integration of coupled P systems to leverage maximum parallelism for efficient learning and fusion.

Main Results:

• The proposed MVAN-CP model demonstrates effectiveness in node classification tasks.
• The attention mechanism facilitates successful collaboration and fusion of multi-view network information.
• Coupled P systems significantly improve the computational efficiency of the learning and fusion processes.

Conclusions:

• MVAN-CP offers a powerful solution for node classification in complex multi-view networks.
• The integration of attention mechanisms and P systems provides a computationally efficient approach.
• The model's effectiveness is validated through experiments on real-world network datasets.