FOBE and HOBE: First- and High-Order Bipartite Embeddings

Understand

Typical graph embeddings may not capture type-specific bipartite graph features that arise in such areas as recommender systems, data visualization, and drug discovery.

• Machine learning methods utilized in these applications would be better served with specialized embedding techniques.
• We propose two embeddings for bipartite graphs that decompose edges into sets of indirect relationships between node neighborhoods.
• When sampling higher-order relationships, we reinforce similarities through algebraic distance on graphs.