We present LinkSAGE, an innovative framework that integrates Graph Neural
Networks (GNNs) into large-scale personalized job matching systems, designed to
address the complex dynamics of LinkedIns extensive professional network. Our
approach capitalizes on a novel job marketplace graph, the largest and most
intricate of its kind in industry, with billions of nodes and edges. This graph
is not merely extensive but also richly detailed, encompassing member and job
nodes along with key attributes, thus creating an expansive and interwoven
network. A key innovation in LinkSAGE is its training and serving methodology,
which effectively combines inductive graph learning on a heterogeneous,
evolving graph with an encoder-decoder GNN model. This methodology decouples
the training of the GNN model from that of existing Deep Neural Nets (DNN)
models, eliminating the need for frequent GNN retraining while maintaining
up-to-date graph signals in near realtime, allowing for the effective
integration of GNN insights through transfer learning. The subsequent nearline
inference system serves the GNN encoder within a real-world setting,
significantly reducing online latency and obviating the need for costly
real-time GNN infrastructure. Validated across multiple online A/B tests in
diverse product scenarios, LinkSAGE demonstrates marked improvements in member
engagement, relevance matching, and member retention, confirming its
generalizability and practical impact.

我们提出了 LinkSAGE，这是一个创新的框架，将图神经网络（GNNs）集成到大规模个性化求职匹配系统中，旨在解决 LinkedIn 广泛专业网络的复杂动态。LinkSAGE 的关键创新之一是其训练和服务方法，它有效地将异构，演化图上的归纳图学习与编码器 - 解码器 GNN 模型相结合。LinkSAGE 在多个在线 A/B 测试中得到验证，展示了会员参与度、相关匹配和会员保留方面的显着改进，证实了其普适性和实际影响。