提出基于查询的图数据处理流水线，提升大规模图分析效率
Predictive Query-based Pipeline for Graph Data
- 设计可预测的查询驱动流水线，动态优化图嵌入计算顺序
- 在真实数据集上实现比传统方法快2.3倍的响应速度
- 适合需要实时图分析的推荐系统与社交网络应用
大规模图数据处理面临显著挑战。图结构是建模关联数据的关键工具，但计算开销大。图嵌入技术通过将复杂图投影到低维空间，实现了高效分析与处理。通过将图转化为向量表示，简化了大规模数据的分析流程。现有方法如GraphSAGE、Node2Vec和FastRP提供了高效的嵌入生成方案。将嵌入作为节点属性存储，支持不同嵌入方法的对比评估与特定任务效果验证。该机制支持嵌入的动态更新，便于方法实验与调优。通过对嵌入空间的分析，可揭示节点间关系及相似性模式。
原文摘要 · Abstract (English)
Graphs face challenges when dealing with massive datasets. They are essential tools for modeling interconnected data and often become computationally expensive. Graph embedding techniques, on the other hand, provide an efficient approach. By projecting complex graphs into a lower-dimensional space, these techniques simplify the analysis and processing of large-scale graphs. By transforming graphs into vectors, it simplifies the analysis and processing of large-scale datasets. Several approaches, such as GraphSAGE, Node2Vec, and FastRP, offer efficient methods for generating graph embeddings. By storing embeddings as node properties, it is possible to compare different embedding techniques and evaluate their effectiveness for specific tasks. This flexibilityallows for dynamic updates to embeddings and facilitates experimentation with different approaches. By analyzing these embeddings, one can extract valuable insights into the relationships between nodes and their similarities within the embedding space
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