连续子图匹配：基于代价模型的动态顶点支配嵌入（技术报告）
Continuous Subgraph Matching via Cost-Model-based Dynamic Vertex Dominance Embeddings (Technical Report)
AI总结：
针对动态图上的连续子图匹配问题，提出基于动态顶点支配嵌入（DIVINE）的通用框架，结合度分组与DAS$^3$概要及剪枝策略，实现高效增量维护与查询，实验验证其高效性。
摘要：
在许多实际应用（如社交网络分析、知识图谱发现、生物网络分析等）中，图数据管理已变得日益重要，并引起了数据库社区的广泛关注。虽然许多图（如Twitter、Wikipedia等）通常随时间演化，但研究连续子图匹配（CSM）问题至关重要，这是一个基础但具有挑战性的图算子，它通过边更新流持续监控动态图上的子图匹配结果。为了高效解决CSM问题，我们精心设计了一个通用的CSM处理框架，基于新颖的动态顶点支配嵌入（DIVINE），该嵌入将顶点邻域映射到嵌入空间，以实现高效子图匹配和动态更新下的增量维护。受高顶点剪枝能力低下的启发，我们提出了一种新的度分组技术，将高度星形模式分解为低度星形子结构组，并在星形子结构组的嵌入上设计了度感知星形子结构概要（DAS$^3$）。我们开发了高效算法，通过遍历DAS$^3$概要并应用我们设计的顶点支配和范围剪枝策略，增量维护动态图并回答CSM查询。通过大量实验，我们确认了所提出的DIVINE方法在真实和合成图上的高效性。
英文摘要：
In many real-world applications such as social network analysis, knowledge graph discovery, biological network analytics, and so on, graph data management has become increasingly important and has drawn much attention from the database community. While many graphs (e.g., Twitter, Wikipedia, etc.) are usually evolving over time, it is of great importance to study the \textit{continuous subgraph matching} (CSM) problem, a fundamental, yet challenging, graph operator, which continuously monitors subgraph matching results over dynamic graphs with a stream of edge updates. To efficiently tackle the CSM problem, we carefully design a general CSM processing framework, based on novel \textit{\underline{D}ynam\underline{I}c \underline{V}ertex Dom\underline{IN}ance \underline{E}mbedding} (DIVINE), which maps vertex neighborhoods into an embedding space to enable efficient subgraph matching and incremental maintenance under dynamic updates. Inspired by low pruning power for high-degree vertices, we propose a new \textit{degree grouping} technique to decompose high-degree star patterns into groups of lower-degree star substructures, and devise \textit{degree-aware star substructure synopses} (DAS$^3$) over embeddings of star substructure groups. We develop efficient algorithms to incrementally maintain dynamic graphs and answer CSM queries by traversing DAS$^3$ synopses and applying our designed \textit{vertex dominance} and \textit{range pruning strategies}. Through extensive experiments, we confirm the efficiency of our proposed DIVINE approach over both real and synthetic graphs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
