PuppyGraph Blog

Learn how to build a knowledge graph from scratch, including data modeling, entity extraction, relationship mapping, graph databases, and best practices for scalable implementation.

Learn how the Louvain algorithm detects communities in large networks using modularity optimization. Explore its applications, benefits, and limitations.

Discover what betweenness centrality is, how it’s calculated, and why it matters in network analysis. Learn the formula, examples, algorithms, and real-world applications.

Compare Neo4j and RedisGraph on architecture, Cypher support, performance, scaling, and ops so you can pick the right graph database for your workload.

A disk database stores data on persistent storage to ensure durability and scale. Learn how disk-based databases work, benefits, and use cases.

Compare key differences in data model, query language (SurrealQL vs Cypher), traversal performance, clustering, and AI-ready hybrid search.

Learn about graph clustering techniques, popular algorithms, and real-world applications in network analysis and machine learning.

Explore the most effective RAG techniques including Naïve, Hybrid, GraphRAG, Agentic, and Multi-Hop RAG. Learn how retrieval-augmented generation improves LLM accuracy, reasoning, and enterprise AI performance.

Explore how LLM knowledge graphs combine large language models with structured data to improve reasoning, accuracy, and explainability in AI systems.

Clustering architecture connects multiple nodes to act as one system for higher availability, better performance, and fault tolerance. Learn types, components, and how it works.

Evaluate data models, schema design, sharding and replication, global distribution, and developer tooling to choose the right platform for production graph workloads.

Learn what agent observability is, how to implement it with logs, metrics, and traces, and how to evaluate LLM agent outcomes across tools, retrieval, and multi-agent workflows. Query telemetry as a graph with PuppyGraph.

Compare GPT vs Neo4j graph database applications across data modeling, querying, accuracy, and use cases to choose the right approach.

Compare FalkorDB vs Neo4j across architecture, performance, scalability, and operational tradeoffs. Learn when to use each graph database.

Learn what an ETL graph is, how it visualizes extract-transform-load workflows, and how graph-based modeling improves data lineage, dependency tracking, and pipeline optimization.