Use Cases

Supply Chain

Adjacency matrices reveal supply chain structure and dependencies at scale:

• Supplier-product mapping: Visualize which suppliers provide which components in a dense, scannable grid. Quickly identify suppliers serving multiple product lines.
• Cross-dependency analysis: See the full matrix of dependencies between facilities, products, and resources. Spot unexpected connections or redundancies.
• Risk clustering: Reorder by geographic region or risk category to see concentrated vulnerabilities as dense blocks.
• Capacity planning: Compare demand patterns across customers and time periods in a structured grid format.

Example: A logistics company uses a matrix view to compare shipping routes between 200 warehouses, instantly identifying underutilized connections and optimizing their distribution network.

Security & Intelligence

In security contexts, matrices excel at revealing systematic patterns:

• Communication pattern analysis: Map who communicates with whom across an organization or suspect network. Dense blocks reveal cliques; sparse areas show isolation.
• Access control auditing: Visualize user-resource permissions as a matrix. Spot over-privileged accounts or unusual access patterns at a glance.
• Transaction monitoring: Display sender-receiver patterns for financial transactions. Structured patterns may indicate automated or coordinated activity.
• Temporal correlation: Compare activity matrices across time windows to detect synchronized behavior or pattern changes.

Example: A fraud analyst examines a transaction matrix reordered by account age, revealing a cluster of new accounts all transacting with the same set of merchants—a classic fraud ring pattern.

Health & Personalized Medicine

Matrices unlock insights in complex healthcare relationships:

• Drug-condition interactions: Map medications against conditions or side effects in a comprehensive grid. Identify dangerous combinations or gaps in treatment protocols.
• Provider referral networks: Visualize referral patterns between specialists. Dense blocks indicate strong collaborative relationships; sparse areas may signal care fragmentation.
• Genomic correlation: Display gene-gene or protein-protein interaction matrices. Cluster analysis reveals functional modules and regulatory pathways.
• Patient similarity: Compare patients across multiple attributes in a similarity matrix. Identify cohorts for personalized treatment strategies.

Example: A research team visualizes protein interaction data as a reordered matrix, revealing three distinct functional clusters that correspond to different cellular processes—guiding their drug targeting strategy.