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graph-algorithms

9 articles tagged with “graph-algorithms”

cypherneo4jgraph-databasesgraph-algorithmsfraud-detectiongdscollaborative-filteringneo4j-gds
graphs
· ~6 min read

Distributed Graph Processing in 2026: Pregel to Trillion-Edge Systems

# Distributed Graph Processing in 2026: Pregel to Trillion-Edge Systems The graph-research corpus contains 409 papers on distributed graph processin

graphs
· ~7 min read

Fraud Detection with Knowledge Graphs: Uncovering Rings, Loops, and Anomalies

## Why Graphs for Fraud Detection? Traditional fraud detection relies on rule-based systems and machine learning on tabular data — flagging transact

graphs
· ~7 min read

Recommendation Engines with Neo4j Knowledge Graphs: From Cypher to Production

## Why Graphs for Recommendations? Recommendation engines have traditionally been dominated by matrix factorisation (Singular Value Decomposition, A

graphs
· ~7 min read

Fraud Detection with Graph Databases: Patterns, Algorithms, and Production Architectures

## Why Graphs for Fraud Detection? Fraud is fundamentally relational. A synthetic identity shares a phone number with a known bad actor. A credit ca

graphs
· ~7 min read

Building Graph-Based Recommendation Systems with Neo4j

## Why Graphs for Recommendations? Most recommendation systems start with a matrix: users as rows, items as columns, and interaction signals (purcha

graphs
· ~8 min read

Community Detection Algorithms in Graphs: From Theory to Production Knowledge Graphs

## Why Community Detection Matters Community detection is the process of partitioning a graph into groups of densely connected nodes — communities —