Graph Algorithms

This is a “graph algorithms topic navigation” page. The goal is not to stack articles together, but to give you an executable learning path from basic traversal to distributed graph computation. Current Directory Status (Topic Structuring Completed) The graph algorithms series has been migrated to: content/zh/dev/algorithm/graph/ It also uses two-digit prefixes (00/10/20...) to mark reading order, which makes it easier to: Browse in sequence within the file system Insert new articles incrementally later (while preserving numbering gaps) Locate stages quickly during batch maintenance Recommended Reading Order (By Capability Building) Stage 0: Traversal Fundamentals (Lay the Foundation First) BFS / DFS Engineering Intro: k-hop Queries, Subgraph Extraction, and Path Reachability Shortest Path in Practice: Engineering Selection of BFS, Dijkstra, and A* Goals: ...

Subtitle / Abstract Subgraph matching is one of the hardest parts of graph querying: NP-hard in theory, but not automatically “too slow” in production. Following the ACERS template, this article explains VF2 and Ullmann clearly, and focuses on what actually decides performance: candidate generation and pruning strategy. Estimated reading time: 15-20 minutes Tags: Subgraph Matching, VF2, Ullmann, Graph Database SEO keywords: Subgraph Isomorphism, VF2, Ullmann, candidate pruning, graph pattern matching Meta description: Starting from NP-hard subgraph isomorphism, this article explains VF2/Ullmann mechanics and practical pruning strategies for constrained graph-database pattern queries. Target Audience Engineers building pattern queries, rule detection, or risk-relationship mining in graph databases Developers who already know BFS/DFS/connected components and want stronger graph-matching skills Algorithm practitioners balancing explainable rule matching against performance limits Background / Motivation In graph databases, you regularly face requirements like: ...

Subtitle / Abstract Connectivity tells you how a graph is partitioned, while PageRank tells you who matters inside each component. This is one of the core advantages of graph databases over relational databases: not only linking data, but propagating structural importance. This article follows ACERS to explain PageRank / PPR principles and production implementation. Estimated reading time: 15-20 minutes Tags: PageRank, PPR, Graph Database, Sparse Matrix SEO keywords: PageRank, Personalized PageRank, sparse matrix, incremental updates, graph database Meta description: From classic PageRank to Personalized PageRank, covering iterative computation, sparse-matrix optimization, and incremental update strategy, with runnable multi-language implementations. Target Audience Engineers building ranking, recommendation, or influence analysis on graph databases Developers who already know BFS/DFS/connected components and want to level up to graph scoring Algorithm engineers focused on iteration performance and update latency on large online graphs Background / Motivation You may already have split graphs into connected components and SCCs, but production systems still face a harder question: ...