Analyzing Heterogeneous Networks With Missing Attributes by Unsupervised Contrastive Learning.

Area of Science:

• Graph representation learning
• Machine learning
• Data mining

Background:

• Heterogeneous Information Networks (HINs) are crucial for modeling complex systems.
• Missing node attributes in HINs significantly degrade performance in representation learning tasks.
• Existing methods struggle with unsupervised learning on HINs lacking attribute information.

Purpose of the Study:

• To develop an unsupervised approach for analyzing HINs with missing attributes.
• To unify attribute completion and representation learning within a single framework.
• To address challenges posed by numerous missing attributes and the absence of labels in unsupervised scenarios.

Main Methods:

• Proposed HGCA (Heterogeneous Graph Contrastive learning for Analysis), an unsupervised contrastive learning framework.
• Introduced an augmented network to capture semantic relationships between nodes and attributes for fine-grained attribute completion.
• Employed a contrastive learning strategy to integrate attribute completion and representation learning.

Main Results:

• HGCA demonstrated superior performance over state-of-the-art methods on three large, real-world HIN datasets.
• The attribute completion by HGCA was shown to enhance the performance of existing HIN models.
• The unsupervised framework effectively handles HINs with extensive missing attributes and no labels.

Conclusions:

• HGCA offers a robust solution for representation learning on HINs with missing attributes.
• The method successfully unifies attribute completion and representation learning in an unsupervised manner.
• HGCA provides a valuable tool for analyzing complex systems modeled by HINs.