TGNN: Graph Neural Networks for Large-Scale Industrial Advertising Scenarios at Tencent

Deploying Graph Neural Networks (GNNs) in industrial advertising is funda- mentally hindered by three intersecting challenges: extreme structural sparsity in cold-start scenarios, hundred-billion-edge scalability limits, and millisecond real-time serving constraints. We present TGNN, an industrial-scale GNN frame- work designed to systematically overcome these bottlenecks. To alleviate severe sparsity, where over 34% of graph nodes have a degree below five, TGNN leverages Large Language Models (LLMs) to construct semantically meaningful connec- tions among sparse nodes, combining this with fine-grained feature learning and cross-domain data augmentation to alleviate the sparsity issue. To support massive graphs exceeding 2 billion nodes and 100 billion edges, we design EasyGraph, a distributed graph database for efficient graph storage and sampling, and An- gelGraph, a large-scale graph computing platform that optimizes GNN training. Furthermore, we propose a hybrid stream-batch pipeline that guarantees strict temporal consistency and low-latency feature propagation. Online A/B testing demonstrates significant business gains: overall GMV increases by 3.43% (new advertisement 4.80%) in social feeds and by 2.77% (new advertisement 3.74%) in short-video feeds. TGNN provides a complete and highly scalable solution for deploying GNNs in real-world advertising recommendation systems.