What is graph analysis?
Connected data and graphs are everywhere—in social and computer networks, process steps, supply chains, and more. A graph consists of nodes and edges. Nodes can represent any object, task, or state, while edges represent the relationships and dependencies between nodes.
Graph analysis is the application of data-driven techniques for the discovery of actionable information, which can be hidden in connected data.
Graph analysis can help organizations:
- Discover areas of interest in data
- Optimize complex systems or processes
- Deliver key information to stakeholders and decision makers in a timely manner
In Tom Sawyer Perspectives, graph analysis algorithms can be run in real time, as automated background processes, or interactively by users.
Graph analysis helps users identify which nodes and edges are most important, making it easier to gain value and intelligence from connected data.
Common types of graph analysis techniques.
Combining graph analysis with visualization supports finding additional insights and facilitates effective communication to stakeholders and decision makers.
Graph analysis is key in analyzing social networks.
Who uses graph analysis?
Graph analysis is applicable across industries in both the private and public sectors. Roles that commonly use graph analysis include:
- Data analysts and data scientists who apply graph analytics to big data problems, identifying clusters, ranking nodes by importance, and building models that account for relational structure.
- Fraud investigators and financial analysts who use path analysis and social network analysis algorithms to trace connections between accounts, transactions, and entities, uncovering fraud rings and suspicious patterns.
- Systems engineers and digital engineers who model complex system architectures, trace dependencies, and identify critical paths or single points of failure in large-scale designs.
- IT and network operations teams who monitor network topology, analyze load and capacity, and support incident response by examining how devices and services are structurally related.
- Law enforcement and intelligence analysts who apply link analysis to map criminal networks and identify key actors.
Graph analysis is particularly impactful in industries where current information is critical, and decisions carry major consequences:
- Manufacturing
- Law enforcement
- IT
- Banking
- Insurance
- Government
“Graphs form the foundation of many modern data and analytics capabilities to find relationships between people, places, things, events and locations across diverse data assets. D&A leaders rely on graphs to quickly answer complex business questions which require contextual awareness and an understanding of the nature of connections and strengths across multiple entities.
"Gartner predicts that by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making across the organization.”
—From “Gartner Identifies Top 10 Data and Analytics Technology Trends”
Graphics Specialized for Your Graph Data Application
Perspectives is optimized with high-speed graphics capabilities and HTML5 canvas graphics to create the best in class graph visualizations for your application. You can use Perspectives HTML classes embed the visualization components into your web application and when the HTML class is assigned to any HTML tag, such as <span> or <div>, the tag will be interpreted as a component, regardless of its type.
Graph analysis and visualization with Tom Sawyer Perspectives
Tom Sawyer Software is an industry expert when it comes to working with connected data. Our graph experts have decades of experience working with enterprises on their most challenging data problems.
Our graph platform, Tom Sawyer Perspectives, addresses big data challenges from two important angles—combining powerful graph analytics tools with revolutionary graph visualization.
Learn how graph analysis and graph visualization techniques reduce the noise in your data:
An example crime network application built with Perspectives.
Tom Sawyer Perspectives puts powerful graph analysis tools at your fingertips
Tom Sawyer Perspectives is a low-code application development platform that enables developers to build custom graph visualization and analysis applications for their specific use cases.
Tom Sawyer Perspectives includes a suite of integrated graph analysis algorithms. Users can apply these algorithms to a graph or selected subgraph without writing code.
The following sections describe the graph analysis algorithms available in Tom Sawyer Perspectives, organized by technique category.
A hierarchy of key graph elements.
Traversal analysis algorithms
Traversal analysis algorithms enable you to understand how best to visit all the nodes of your graph in an effective order. These techniques can be used in systems modeling and verification applications or in combination with other graph analysis techniques.
Traversal algorithms included with Tom Sawyer Perspectives:
- Breadth First Search
- Depth First Search
- Topological Sort
Clustering analysis algorithms
Clustering analysis algorithms identify areas of higher connectivity by finding natural clusters, or groups, within the topology of a graph. These algorithms are useful for operations and enterprise architecture management.
Clustering algorithms included with Tom Sawyer Perspectives:
- Clustering
- k-Core
- m-Slice
Partitioning analysis algorithms
Partitioning analysis algorithms divide a graph into components to help users identify natural groupings in connected data. The resulting components can be analyzed separately using other graph analysis techniques. Partitioning can also help identify vulnerabilities in system models and computer networks.
Partitioning algorithms included with Tom Sawyer Perspectives:
- Connected Components
- Biconnected Components
- Strongly Connected Components
Path analysis algorithms
Path analysis algorithms identify routes and connections between graph elements. They can help users find the most efficient or most important paths from one graph element to another. These techniques are useful in law enforcement, finance, and life sciences investigations that require an understanding of how entities are related.
Path algorithms included with Tom Sawyer Perspectives:
- All Pairs Shortest Path
- Bridge Detection
- Disjoint Paths
- Path Exists
- Reachable Nodes
- Root Cause
- Shortest Paths
- Simple Paths
- Sorted Paths
- Unique Path
Cycle analysis algorithms
Cycle analysis algorithms identify cycles and circular dependencies in graphs. They can be used to find or remove circular dependencies before applying additional graph analysis techniques, such as Topological Sort or Maximum Flow.
Cycle algorithms included with Tom Sawyer Perspectives:
- Acyclic Test
- Cycle Breaking Edge Detection
- New Cycle Test
Social network analysis algorithms
Social network analysis algorithms rank nodes based on different measures of importance, centrality, or influence. These algorithms help users identify significant entities and understand their structural roles within a connected network.
Social network analysis algorithms included with Tom Sawyer Perspectives:
- Augmented Centrality
- Betweenness Centrality
- Closeness Centrality
- Degree Centrality
- Eigenvector Centrality
- PageRank Centrality
Network flow analysis algorithms
Network flow analysis algorithms analyze how resources or capacity move through a connected system. They can identify maximum throughput, minimum-cost flows, and vulnerabilities in a network. These algorithms are useful for manufacturing, logistics, and telecommunications system management.
Network flow algorithms included with Tom Sawyer Perspectives:
- Maximum Flow
- Minimum Cost Flow
- Minimum Cut
Tree analysis algorithms
Tree analysis algorithms determine whether a graph is a tree and can identify a minimum spanning tree within a larger connected graph. This tree structure can form the backbone of a graph and support computer and telecommunications network applications.
Tree algorithms included with Tom Sawyer Perspectives:
- Minimum Spanning Tree
- Tree Test
Hands-on graph analysis examples
Built using Tom Sawyer Perspectives, these interactive examples demonstrate how graph analysis algorithms can reveal patterns, trace paths, and identify important elements in connected data. Sign up to explore each application.
