Searching to extrapolate embedding for out-of-graph node representation learning.

Area of Science:

• Graph Representation Learning
• Machine Learning
• Network Science

Background:

• Out-of-graph node representation learning is crucial for dynamic graphs in applications like recommendation systems and malware detection.
• Existing methods struggle with fixed in-graph node embeddings and data diversity, limiting performance.
• Addressing these limitations is essential for advancing dynamic graph analysis.

Purpose of the Study:

• To develop a novel approach for learning representations of newly arrived nodes in dynamic graphs.
• To overcome performance limitations of existing methods caused by fixed in-graph embeddings and data diversity.
• To introduce a neural architecture search (NAS) based solution for out-of-graph node embedding extrapolation.

Main Methods:

• Formulated the problem as a neural architecture search (NAS) problem.
• Proposed Searching to Extrapolate Embedding (S2E), a framework utilizing transition and aggregation modules for embedding extrapolation.
• Implemented objective transformation to handle non-differentiable metrics and enhance NAS efficiency for data diversity.

Main Results:

• S2E demonstrated outstanding performance on real-world datasets.
• Experimental validation confirmed the effectiveness of the proposed search space and algorithm within S2E.
• The method successfully extrapolates embeddings for out-of-graph nodes based on neighbor embeddings.

Conclusions:

• S2E offers a significant advancement in out-of-graph node representation learning for dynamic graphs.
• The NAS-based approach effectively handles challenges of fixed embeddings and data diversity.
• This work provides a robust and efficient solution for dynamic graph analysis and related applications.