RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation

[Submitted on 16 Jun 2026 (v1), last revised 24 Jul 2026 (this version, v4)]

Authors:Renzhi Wu, Zikun Cui, Junjie Yang, Tai Guo, Hong Li, Xian Chen, Li Yu, Ke Pan, Sri Reddy, Mahesh Srinivasan, Nipun Mathur, Haomin Yu, Hong Yan

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Abstract:Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems — graph construction, representation learning, and real-time serving — yet existing work addresses each in isolation. We present RankGraph-2, a framework deployed at Meta that co-designs all three lifecycle stages for similarity-based retrieval (U2U2I and U2I2I), where each stage’s requirements shape the others. Serving requires a co-learned cluster index to avoid expensive online KNN — this pushes index co-training into the training objective. Training benefits from the observation that similarity-based retrieval tolerates pre-computed neighborhoods, eliminating online graph infrastructure — this requires construction to produce self-contained data. Construction must also support hour-level refresh for item coverage. Acting on these cascading requirements, RankGraph-2 reduces hundreds of trillions of edges to hundreds of billions via subsampling with popularity bias correction, pre-computes multi-hop neighborhoods via personalized PageRank, and co-learns a residual-quantization cluster index that reduces serving computational cost by 83%. This lifecycle co-design enables a simple architecture to achieve 3.8 x higher recall than a GAT + Deep Graph Infomax model on a bipartite graph and 2.1 x higher than PyTorch-BigGraph on item retrieval. RankGraph-2 delivers up to +0.96% CTR and +2.75% CVR, and has powered 20+ retrieval launches across major surfaces.

Submission history

From: Zikun Cui [view email]
[v1] Tue, 16 Jun 2026 18:27:11 UTC (323 KB)
[v2] Fri, 26 Jun 2026 18:15:16 UTC (118 KB)
[v3] Sat, 11 Jul 2026 01:41:34 UTC (117 KB)
[v4] Fri, 24 Jul 2026 17:39:14 UTC (118 KB)