Graph Analytics in 2026: Top 6 Use Cases & Tools
Most analytics focuses on numbers, revenue trends, customer counts, and conversion rates. Graph analytics takes a different approach: it examines how things connect to each other.
Think about how Facebook knows which friends to suggest, or how investigators traced the Panama Papers to reveal hidden offshore accounts. These insights come from analyzing relationships, not just data points.
Graph types:
Type | Description | Use Case |
|---|---|---|
Directed | Edges point one way (A→B doesn’t mean B→A) | Twitter follows, payment flows |
Undirected | Connections work both ways | Facebook friendships, road networks |
Weighted | Edges have values (distance, frequency, strength) | Shipping routes, call frequency |
Cyclic | Paths loop back to starting nodes | Web page links, circular dependencies |
What Makes Graph Analytics Different
Traditional databases store information in rows and columns. Graph databases store connections. A customer isn’t just a record with attributes; it’s a node connected to purchases, other customers, locations, and behaviors.
Basic components:
- Nodes (vertices): The entities, people, companies, transactions, and products
- Edges (links): The relationships between them “purchased,” “knows,” “transferred money to”
How Organizations Use Graph Analytics
1. Finding Hidden Networks (Journalism & Compliance)
The International Consortium of Investigative Journalists used graph analytics to crack the Panama Papers. They had thousands of documents showing shell companies, but the real challenge was connecting the dots: who actually owned these companies?
Graph analytics let them map relationships across layers of corporate structures. Company A was owned by Company B, which was owned by Company C, which traced back to a politician’s family member. These multi-hop connections would be nearly impossible to spot in spreadsheets.
Banks use similar techniques for compliance. If Customer X sends money to Company Y, and Company Y has ties to a sanctioned entity three steps removed, graph analytics catches it. Traditional transaction monitoring wouldn’t.
2. Catching Fraud (Telecom & Finance)
Telecom fraud costs the industry $40 billion yearly. Fraudsters create networks of accounts that call each other, rack up charges, then disappear. Graph analytics spots these rings by identifying clusters of accounts that:
- Were created around the same time
- Call each other mostly
- Show similar usage patterns
- Have connections to previously flagged fraud
One fraudulent account looks normal. Ten connected accounts with identical behavior patterns trigger alerts.
3. Optimizing Routes (Supply Chain & Utilities)
Airlines don’t just look for the shortest distance between cities, they analyze the entire network. Which routes feed into hub airports? Where do delays cascade through the system? Graph analytics calculates optimal flight paths considering fuel costs, aircraft positioning, crew schedules, and passenger connections simultaneously.
Electric companies map power grids as graphs. When a transformer fails, shortest-path algorithms reroute power through alternative connections. During peak demand, they balance load across the network to prevent cascading failures.
4. Tracking Disease Spread (Healthcare)
During COVID-19, several governments built contact-tracing apps. If Person A tested positive, the system mapped everyone they’d been near. Then it checked those people’s contacts, and their contacts, expanding outward.
Chinese company We-Yun built a Neo4j-based app that allowed citizens to enter their information to see whether they’d crossed paths with confirmed cases. The graph could trace transmission chains back to identify super-spreader events.
5. Powering Recommendations (Marketing & Social Networks)
When Spotify suggests songs, it’s not just matching genres. It builds a graph:
- Users who liked Song X also liked Songs Y and Z
- You listened to Song X
- You share listening patterns with User Group A
- User Group A recently played Song Q
This collaborative filtering works because musical tastes form natural clusters. People with similar preferences connect in the graph, even if they’ve never met.
LinkedIn’s “People You May Know” works similarly. It looks at:
- Mutual connections (friends of friends)
- Shared employers, schools, locations
- Profile viewers
- Similar job titles or industries
The algorithm ranks suggestions by connection strength, not just the number of shared contacts.
6. Detecting National Security Threats
Intelligence agencies analyze communication patterns to identify threats. If Suspect A calls Person B, who texts Person C, who emails Person D, and Person D has known terrorist connections, that’s a pattern worth investigating.
The controversy comes from collecting data on non-suspects. Graph analytics requires mapping the entire network to find these multi-hop relationships, which means analyzing millions of innocent people’s communications.
Core Graph Analytics Methods
Rather than listing methods generically, here’s what each actually does:
- Centrality analysis answers “who matters most?” in a network. Degree centrality counts direct connections (who knows the most people). Betweenness centrality finds gatekeepers (who control information flow between groups). Closeness centrality identifies people positioned near everyone else (who can spread information fastest).
- Community detection finds natural groupings. In customer networks, it segments people by actual behavior patterns rather than demographic assumptions. One retail bank found its high-value customers formed three distinct communities based on transaction patterns, none of which matched their existing customer segments.
- Path analysis calculates optimal routes. Shipping companies use it to minimize fuel costs and delivery time across their entire network, not just individual routes. The shortest path between Warehouse A and Customer B might route through Distribution Center C to consolidate shipments.
- Graph traversal systematically explores networks. Network engineers use Breadth-First Search to diagnose connectivity issues, checking immediate neighbors first, then their neighbors, and expanding outward until they find the break.
- Connectivity analysis measures relationship strength. How many paths exist between two nodes? How quickly can information flow between them? If you remove certain nodes, does the graph fragment?
Graph Database Tools
The tool landscape breaks into several categories:
Enterprise-scale databases: Neo4j handles complex, real-time analytics on large datasets. Many Fortune 500s use it for fraud detection and recommendation engines. Available as open source or commercially, with enterprise support.
TigerGraph focuses on speed for massive graphs. Financial institutions use it to analyze billions of transactions in near real-time. Better for write-heavy workloads than Neo4j.
Cloud-managed services: Amazon Neptune runs on AWS infrastructure. You don’t manage servers, just query your graph. Supports both property graphs and RDF graphs for linked data applications.
Azure Cosmos DB offers graph capabilities alongside document and key-value storage. Choose it if you need multiple data models in one system or want tight Azure integration.
Big data integration: GraphX sits inside Apache Spark, so you can run graph computations alongside your existing Spark data pipelines. ML teams use it to generate graph-based features for training models.
Google Vertex AI enables machine learning models that incorporate graph features, such as node embeddings and relationship scores. Different from a pure graph database, it’s for building AI applications that use graph-structured data.
Specialized tools: JanusGraph is open-source and distributed, scaling across multiple machines. Good for teams that want control over infrastructure without vendor lock-in.
ArangoDB combines graph, document, and key-value storage. One database, three query patterns. Useful for applications with mixed data needs.
Linkurious visualizes complex graphs, particularly for fraud investigation. Analysts can visually explore suspicious transaction networks rather than write queries.
Gephi gives data scientists visualization capabilities for network analysis. Open-source and designed for research use.
FAQ
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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Thanks for this overview. Do you know of any libraries that can be used to create graph-like visualizations without a graph database? Can a graph database be used to produce the 2d headliner image for this article?
To prepare a 2d graphic, a graph database would be a bit too much effort. If you need something like the 2D headliner image of this article, you can use a Javascript charting library like Plotly which can do X Y scatter with lines.