Scaling Graph Neural Networks for Friend Recommendation: Multi-Hash User Embeddings and Temporal Neighbor Sampling
Advanced techniques for scaling graph neural networks to production-level social graphs with hundreds of millions of users.
AI Summary
Researchers present a scalable GNN system for friend recommendation that reduces table size by 98% and improves temporal sampling efficiency, achieving 16% more friend additions in production tests.
Excerpt
Friend recommendation is inherently graph-structured: the relevance of a potential connection depends on multi-hop social context rather than user attributes alone. However, deploying message-passing GNNs on a production-scale social graph with hundreds of millions of users and tens of billions of edges requires addressing numerous modeling and systems challenges. We present a scalable end-to-end GNN ranking system for production social graphs, focusing on two design choices that are critical in