Effective Deep Attributed Network Representation Learning With Topology Adapted Smoothing.

Area of Science:

• Computer Science
• Graph Theory
• Machine Learning

Background:

• Attributed networks, like social networks, are common.
• Node attributes are crucial for improving network representation learning.
• Existing methods face an
• oversmoothing
• problem due to fixed smoothing parameters.

Purpose of the Study:

• To tackle the oversmoothing problem in attributed network representation learning.
• To develop a method that adapts to diverse network topological characteristics.
• To generate robust and distinguishable node features.

Main Methods:

• Introduced an adaptive smoothing parameter based on network topology (e.g., small worldness, node convergency).
• Developed an integrated autoencoder for node representation learning.
• Reconstructed a combination of smoothed structure and attribute information.

Main Results:

• The proposed method effectively smooths node attributes and structure information adaptively.
• Achieved robust and distinguishable node features tailored to different networks.
• Outperformed state-of-the-art methods in preserving intrinsic network information across various datasets.

Conclusions:

• The adaptive smoothing approach mitigates the oversmoothing issue in attributed network representation learning.
• The method demonstrates superior performance in capturing network characteristics compared to existing techniques.
• This work offers a more effective way to learn node representations for networks with varied topological properties.