Graph intelligence refers to the application of analytical and computational techniques to extract insights from graph data structures. It involves analyzing relationships and patterns within networks, where entities are represented as nodes and their connections as edges. This approach is critical in areas like social network analysis, bioinformatics, supply chain management, and fraud detection.
Graph intelligence leverages algorithms to understand complex interdependencies, identify clusters, and predict trends in networked data, offering deeper insights than traditional linear data analysis methods.
Graph intelligence can help you:
A graph intelligence dashboard for global enterprise IT Management.
Graph intelligence reveals intricate and often hidden connections within large datasets.
Graph intelligence highlights relationships enabling discovery of key relationships in your data.
Graph intelligence provides insights that enhance communication of results to stakeholders and leads to more informed decisions.
Graph intelligence network analysis can forecast future trends and behaviors providing powerful insight for your business.
Graph intelligence identifies inefficiencies and opportunities in networked systems like supply chains, communication networks, or social networks.
Graph intelligence aids in the detection of unusual patterns, which is valuable in fraud detection and security.
Graph intelligence is multifaceted. There are a number of fundamental elements that make up the foundation of graph intelligence. It is important for anyone planning to use graph intelligence to gain an understanding of its complexities and capabilities.
Nodes and edges are the foundation of graph intelligence. Nodes represent entities in the network, for example, people, places, and things. Edges represent relationships or interactions between nodes in networks, fraud rings, organizational hierarchies, and more. Paths that use a sequence of edges to connect two nodes can show indirect, yet important, relationships.
Data can reside in a graph database, which is designed to efficiently store and manage graph data. However, data may reside in a relational database or a text-based format. Often, data may be located across several different types of data stores, requiring federation of the data for effective analysis. Data integration and federation combine data from various sources to enrich the graph and enable deeper insight considering multiple facets.
Graph visualization visually represents the network of nodes and edges, and is vital to understanding the graph structures and patterns in data. Effective graph visualization can provide flexibility in how nodes and edges are represented, such as customizable node and edge UIs. A dashboard view of graph and data visualizations can aggregate information into a centralized view of information, enabling users to see a snapshot of all key information at once, for increased efficiency and time-saving.
Graph analytics help analysts find what is important in the data. Graph analysis techniques include traversals, clustering, partitioning, path, cycle, social network, network flow, and tree analysis algorithms. Graph analysis helps find important patterns, discover areas of interest in data, optimize complex systems or processes, determine root cause, and more.
Interactive graph navigation enables exploration of data in real-time, and facilitates discovery. It also enables analysts to focus on the parts of the data that they are interested in, without being distracted by extraneous information. Interaction techniques can include expanding and collapsing of nested drawings to show more or less detail, and drilling in or out to focus on a particular graph or sub-graph.
Graph intelligence for supply chain management.
Graph intelligence is applicable to industries in the private and public sectors. Data analysts and data scientists rely on graph analytics to solve big data problems in digital transformation, digital engineering, supply chain, logistics, social network analysis, and fraud detection and prevention.
Graph intelligence is impactful in many industries where having up-to-the-minute information is crucial and decisions have major consequences:
To have best results in applying graph intelligence to problems, analysts must:
The application of graph intelligence tailored for a specific use case.
Graph intelligence analysts must surmount several obstacles in order to discover important insights.
First, analysts often are not sure exactly how to apply graph technology to their data. And, when they do start to use graph technology, they may apply techniques that are not the best for their use case. For example, someone new to graph intelligence may look for direct connections instead of also searching for paths, which are useful for discovering more relationships.
Poor visualization or analysis that is not powerful enough for the use case also hampers the understanding of results and navigation of data. And an additional challenge is collecting of graph analytics without context that explains their meaning and defines their importance.
Analysts need effective and easy-to-use graph intelligence technology that provides necessary graph tools. This platform must provide:
Use graph intelligence to understand important paths in your data.
Navigate to relevant data with the assistance of graph intelligence.
Perspectives provides best-in-class graph layout to create useful and readable visualizations of complex data.
Perspectives graph analytics library provides graph intelligence algorithms that can be run in real-time through automated background processes, or interactively by users.
Perspectives query builder allows analysts to load data from graph databases without the need to know Gremlin or Cypher query languages.
Perspectives load neighbors feature provides analysts control over the data they see to support efficient navigation of data during intelligence gathering.
Perspective's timeline view helps analysts see when key events occurred adding context to their analysis.
Perspectives filters provide a way for analysts to focus on key results.
An important aspect of graph intelligence is how users engage with graph-based systems and how these systems fetch and display data. The ability to interact with data and analytics results provides more useful explorations through data and allows intelligence analysts to discover additional insights in real-time.
This navigation through data is supported by populating the data model from any data source, whether graph, relational, or text-based data sources, and an application with a user-friendly interface that provides tools for analysts to build queries and interactively load data.
User interaction is an important aspect of graph intelligence
For successful interaction, analysts must be able to construct complex queries to retrieve specific data from a graph database. The Perspectives query builder simplifies accessing targeted information through a user-friendly interface, without the need for the analysts to know a technical query language. Users can specify criteria, relationships, and attributes to filter and retrieve data efficiently and effectively, enabling precise data extraction from complex network structures, and facilitating advanced analysis and decision-making.
Methodologies and tools that enable intelligence analysts to intuitively traverse and understand complex network structures include techniques such as filtering, searching, and clustering algorithms, which allow users to focus on specific parts of the graph. Graph intelligence platforms such as Perspectives employs dynamic layouts and responsive design elements that adjust to user interactions, making the exploration of large and intricate networks manageable and insightful. With effective data navigation, analysts are able to effectively navigate and decipher the wealth of information embedded in graph data.
User-friendly interfaces allow intuitive exploration of complex graph data. Interactive elements like zooming, panning, and clicking on nodes and edges for detailed information, and more advanced interaction like expanding, collapsing, hiding, and showing nodes is essential. In addition, effective graph visualization and highlighting of analytics results are necessary for graph intelligence software so that intelligence analysts can focus on key parts of the data and communicate key findings.
Creating a graph data model tailored to your use case is key to discovering important results. This begins with the definition of a schema to understand the type of data and attributes that will be analyzed.
It is common for data to be contained in any number of different data stores, either graph databases or non-graph data sources, such as SQL, RESTful endpoints, JSON, XML, Excel, and structured text files.
The Tom Sawyer Perspectives graph intelligence platform is data agnostic so that you can build a single schema and apply graph technology to data no matter where it resides. It meets the needs of intelligence analysts by providing a way to import data, perform graph compute, see results, and load related data.
Supported data sources include:
Perspectives supports data federation and integration from a wide range of data sources.
The types of graphs used in intelligence range from simple undirected graphs to complex hierarchical and multilayered structures. Each graph type may be suited to different analytical needs and scenarios. Understanding these variations is crucial for effectively applying graph intelligence in real-world problems, enhancing the clarity and precision of data interpretation.
The different graph types are as follows:
Each type provides a unique lens for examining and interpreting specific aspects of interconnected data.
Weighted graph showing relationships of varying strength in the flow of commodities.
Directed graph of a supply chain.