Engineering Practice

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 In dynamic-graph workloads, the real pain point is not “do you know the algorithm,” but “can the system survive continuous updates.” Following the ACERS template, this article explains three engineering essentials: incremental shortest path, incremental PageRank, and connectivity maintenance, along with three practical strategies: local recomputation, lazy updates, and approximate results. Estimated reading time: 14-18 minutes Tags: dynamic graph, incremental computation, shortest path, PageRank, connectivity maintenance SEO keywords: dynamic graph, incremental shortest path, incremental PageRank, connectivity maintenance, local recomputation, lazy updates, approximate results Meta description: An engineering guide to dynamic graphs: how to control latency and cost in high-frequency update scenarios with incremental algorithms and practical system strategies. Target Audience Engineers building online services for graph databases, relationship graphs, and recommendation graphs Developers moving from offline graph computation to real-time incremental computation Tech leads who want to replace “full recomputation” with a production-ready update pipeline Background / Motivation Static graph algorithms look elegant in papers, but real production graphs are constantly changing: ...

Subtitle / Abstract Centrality is not just a paper concept. In graph systems, it is a practical node-importance ranking engine. This article follows the ACERS structure to explain Degree / Betweenness / Closeness and gives one pragmatic conclusion: most online systems reliably support only Degree + approximate Betweenness. Estimated reading time: 12-16 minutes Tags: Graph Theory, Centrality, Degree, Betweenness, Closeness SEO keywords: graph centrality, Degree Centrality, Betweenness, Closeness, approximate Betweenness Meta description: Engineering guide to graph centrality: definitions, complexity, approximation methods, and production strategies, with runnable code. Target Audience Engineers working on relationship graph analysis, knowledge graphs, or graph-database query optimization Developers who need to turn “node importance” from concept into production metric Practitioners who want to understand why Betweenness is expensive in production and how to approximate it Background / Motivation In graph systems, you will eventually face questions like these: ...

Subtitle / Abstract Shortest path is not one question. It is an engineering skill set: choose the right algorithm by graph conditions. This ACERS article breaks down BFS (unweighted) / Dijkstra (non-negative weights) / A (heuristic)* and gives optimization templates you actually use in relationship graphs, recommendation paths, and path explanations. Estimated reading time: 14-18 minutes Tags: Graph Theory, shortest path, BFS, Dijkstra, A* SEO keywords: shortest path, BFS, Dijkstra, A*, bidirectional search, multi-source BFS Meta description: Engineering guide to the shortest-path core trio: algorithm boundaries, complexity, runnable code, optimization strategies, and practical scenarios. Target Audience Learners reinforcing graph fundamentals who want reusable engineering templates Backend/algorithm engineers working on social links, recommendation paths, or graph-query explanations Developers who know BFS, Dijkstra, and A* by name but still struggle with robust selection and optimization Background / Motivation Shortest-path problems are common in: ...