<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>图数据库 on Jeanphilo Blog</title><link>https://shio-chan-dev.github.io/jeanblog/zh/tags/%E5%9B%BE%E6%95%B0%E6%8D%AE%E5%BA%93/</link><description>Recent content in 图数据库 on Jeanphilo Blog</description><generator>Hugo -- 0.159.2</generator><language>zh-cn</language><lastBuildDate>Mon, 09 Feb 2026 10:14:45 +0800</lastBuildDate><atom:link href="https://shio-chan-dev.github.io/jeanblog/zh/tags/%E5%9B%BE%E6%95%B0%E6%8D%AE%E5%BA%93/index.xml" rel="self" type="application/rss+xml"/><item><title>图算法专题学习路径：从 BFS 到图计算模型</title><link>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/00-graph-algorithms-learning-path/</link><pubDate>Mon, 09 Feb 2026 10:14:45 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/00-graph-algorithms-learning-path/</guid><description>图算法专题导航与推荐阅读顺序，覆盖 BFS/DFS、可达性、最短路、CC/SCC、中心性、PageRank、社区发现、子图匹配、动态图、图分区与图计算模型。</description></item><item><title>图分区算法：Edge-cut vs Vertex-cut 与 METIS 工程解析</title><link>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/100-graph-partitioning-edge-cut-vertex-cut-metis/</link><pubDate>Mon, 09 Feb 2026 10:04:05 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/100-graph-partitioning-edge-cut-vertex-cut-metis/</guid><description>面向生产级图数据库的图分区实战文章，覆盖 Edge-cut 与 Vertex-cut 对比、METIS 核心流程、可运行示例和工程调优清单。</description></item><item><title>子图匹配 / 模式匹配：VF2 与 Ullmann 的工程化剪枝 ACERS 解析</title><link>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/70-subgraph-matching-vf2-ullmann-and-pruning/</link><pubDate>Mon, 09 Feb 2026 09:59:16 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/70-subgraph-matching-vf2-ullmann-and-pruning/</guid><description>系统讲解 Subgraph Isomorphism（NP-hard）与 VF2/Ullmann 核心思想，重点强调工程现实：受限模式查询与候选剪枝通常比算法名称本身更重要。</description></item><item><title>图中心性三件套：Degree、Betweenness、Closeness 工程 ACERS 解析</title><link>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/50-graph-centrality-degree-betweenness-closeness/</link><pubDate>Mon, 09 Feb 2026 09:56:11 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/50-graph-centrality-degree-betweenness-closeness/</guid><description>系统讲解图中心性三大指标：Degree、Betweenness、Closeness。重点给出工程可落地结论：大多数系统优先支持 Degree 和近似 Betweenness，并说明复杂度、近似策略与上线取舍。</description></item><item><title>PageRank / Personalized PageRank：图数据库节点重要性与增量更新 ACERS 解析</title><link>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/60-pagerank-and-personalized-pagerank/</link><pubDate>Mon, 09 Feb 2026 09:54:25 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/60-pagerank-and-personalized-pagerank/</guid><description>系统讲解 PageRank 与 Personalized PageRank：从迭代式计算、稀疏矩阵实现到增量更新策略，覆盖推荐与影响力分析等图数据库核心场景。</description></item><item><title>连通分量与强连通分量：Tarjan / Kosaraju 工程 ACERS 解析</title><link>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/40-connected-components-and-scc-tarjan-kosaraju/</link><pubDate>Mon, 09 Feb 2026 09:50:22 +0800</pubDate><guid>https://shio-chan-dev.github.io/jeanblog/zh/dev/algorithm/graph/40-connected-components-and-scc-tarjan-kosaraju/</guid><description>系统讲解无向图 Connected Components 与有向图 SCC，重点覆盖 Tarjan（工程常用）与 Kosaraju，对应图数据库中的社区划分、子图切分与分片 hint。</description></item></channel></rss>