Proximity-Based Compression for Network Embedding.

Area of Science:

• Graph theory
• Network analysis
• Machine learning

Background:

• Network embedding is crucial for graph analysis, but large graph sizes and noise pose challenges.
• Existing methods struggle with scalability and accuracy on massive or noisy networks.

Purpose of the Study:

• To introduce a novel Network Embedding (NECL) method that enhances efficiency and effectiveness.
• To investigate if network compression improves learning and representation quality.
• To address scalability and accuracy issues in graph embedding.

Main Methods:

• Proposed a graph compression technique based on neighborhood similarity to create super-nodes.
• Employed the compressed graph for network embedding to reduce computational cost and capture global structure.
• Refined embeddings from the compressed graph back to the original graph structure.

Main Results:

• Demonstrated that network compression significantly boosts learning efficiency.
• Showcased improved representation quality and classification accuracy on large real-world graphs.
• Validated NECL's effectiveness across various state-of-the-art embedding algorithms like DeepWalk, Node2vec, and LINE.

Conclusions:

• NECL offers a general meta-strategy for efficient and effective network embedding.
• The proposed compression method successfully mitigates challenges associated with large and noisy graphs.
• NECL enhances both the speed and accuracy of network analysis applications.