用图神经网络解决社交网络好友推荐的超大规模难题
GNN Applied to Ego-nets for Friend Suggestions
- 将大图问题拆解为多个小规模局部网络任务，提升可扩展性
- 在真实数据集上超越所有基线模型，线上测试带动业务指标增长
- 专为动态、无特征的社交网络设计，适合工业级推荐场景
社交网络好友推荐面临社交图谱超大规模（数亿人、数十亿连接）的挑战。传统基于启发式或矩阵分解的方法虽具可扩展性，但因无监督导致效果不佳。本文提出通用的邻域网络好友评分框架（Generalized Ego-network Friendship Score），将全图链接预测转化为一系列局部邻域网络上的低尺度任务，并聚合结果。核心模型以邻域网络为输入，输出节点间相关性矩阵。我们进一步设计了适用于社交网络的WalkGNN模型，能有效处理异质、动态且无特征的图级链接预测任务。为评估模型，构建了精确反映现实问题的Ego-VK数据集。离线实验表明，本模型优于所有基线方法；线上A/B测试验证了其对业务指标的积极影响。
原文摘要 · Abstract (English)
A major problem of making friend suggestions in social networks is the large size of social graphs, which can have hundreds of millions of people and tens of billions of connections. Classic methods based on heuristics or factorizations are often used to address the difficulties of scaling more complex models. However, the unsupervised nature of these methods can lead to suboptimal results. In this work, we introduce the Generalized Ego-network Friendship Score framework, which makes it possible to use complex supervised models without sacrificing scalability. The main principle of the framework is to reduce the problem of link prediction on a full graph to a series of low-scale tasks on ego-nets with subsequent aggregation of their results. Here, the underlying model takes an ego-net as input and produces a pairwise relevance matrix for its nodes. In addition, we develop the WalkGNN model which is capable of working effectively in the social network domain, where these graph-level link prediction tasks are heterogeneous, dynamic and featureless. To measure the accuracy of this model, we introduce the Ego-VK dataset that serves as an exact representation of the real-world problem that we are addressing. Offline experiments on the dataset show that our model outperforms all baseline methods, and a live A/B test demonstrates the growth of business metrics as a result of utilizing our approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。