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The Complete Guide to Building with Memgraph and MAGE in 2026: Graph Algorithms for Real-World Problems

The Complete Guide to Building with Memgraph and MAGE in 2026: Graph Algorithms for Real-World Problems

Memgraph emerged as the open-source graph database for real-time analytics in 2025-2026, competing with Neo4j by offering built-in graph algorithms through the MAGE (Memgraph Advanced Graph Extensions) library. For fraud detection, recommendation engines, and network analysis, Memgraph's in-memory engine delivers sub-millisecond query responses.

Here's the practical guide.

Installation 

# Docker
docker run -it --rm -p 7687:7687 -p 7444:7444 -p 3000:3000 memgraph/memgraph:latest

# Or use Memgraph Lab (GUI)
# Download from memgraph.com
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Cypher Query Language 

-- Same syntax as Neo4j
CREATE (alice:Person {name: "Alice", trust_score: 0.9})
CREATE (bob:Person {name: "Bob", trust_score: 0.3})
CREATE (charlie:Person {name: "Charlie", trust_score: 0.7})

CREATE (alice)-[:TRUSTS {amount: 0.8}]->(bob)
CREATE (bob)-[:TRUSTS {amount: 0.6}]->(charlie)
CREATE (alice)-[:TRUSTS {amount: 0.9}]->(charlie)
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PageRank 

-- Run PageRank algorithm
CALL pagerank.get()
YIELD node, rank
RETURN node.name AS name, rank
ORDER BY rank DESC;
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Community Detection (Label Propagation) 

-- Find communities in the graph
CALL community_detection.label_propagation()
YIELD node, community_id
WITH community_id, collect(node.name) AS members
RETURN community_id, members;
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Shortest Path 

-- BFS shortest path
MATCH path = shortestPath((a:Person {name: "Alice"})-[*]-(b:Person {name: "Charlie"}))
RETURN path;

-- Weighted shortest path
CALL graph_tool.bellman_ford(
  {start_node: (Person {name: "Alice"}), 
   end_node: (Person {name: "Charlie"}),
   weight_property: "amount"}
) YIELD path, cost
RETURN path, cost;
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MAGE: Graph Algorithms Library 

-- PageRank with parameters
CALL pagerank.get(
  {iterations: 100, damping: 0.85}
) YIELD node, rank
WITH node, rank
WHERE rank > 0.01
RETURN node.name, rank
ORDER BY rank DESC
LIMIT 20;

-- Betweenness centrality
CALL betweenness_centrality.get()
YIELD node, centrality
RETURN node.name, centrality
ORDER BY centrality DESC
LIMIT 10;

-- Top k-core decomposition
CALL kcore.get({k: 3})
YIELD node, kcore
RETURN node.name, kcore
ORDER BY kcore DESC;
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Fraud Detection Pattern 

-- Find suspicious patterns: many transactions to the same node
MATCH (a)-[t:TRANSFER]->(b)
WITH b, count(t) AS transaction_count, sum(t.amount) AS total_amount
WHERE transaction_count > 10 AND total_amount > 10000
RETURN b.name, transaction_count, total_amount
ORDER BY total_amount DESC;
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Integration with Python 

from memgraph import Memgraph

mg = Memgraph(host="localhost", port=7687)

# Run query
results = mg.execute_and_fetch("""
    MATCH (a)-[t:TRUSTS]->(b)
    RETURN a.name, b.name, t.amount
    ORDER BY t.amount DESC
    LIMIT 10
""")

for row in results:
    print(f"{row['a.name']} trusts {row['b.name']}: {row['t.amount']}")
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