Have you ever wondered how to make sense of the complex relationships in your data? How to solve a complex crime with multiple data sources, uncover complex financial fraud, or the optimal routes for delivery trucks? Graph analytics, a powerful technique, can help you uncover hidden patterns and insights from your data.
What is Graph Analytics?
Graph analytics, also known as graph algorithms, are analytic tools that can measure and explore the connections between objects in a graph. A graph is a data structure that consists of nodes (entities) and edges (relationships) between them. For example, a graph of a co-offending criminal network might have nodes representing people and edges representing phone numbers, accounts, or messages.
Graph analytics can reveal information that is not easily accessible from traditional analytics methods, such as:
- Centrality analysis: the importance or influence of nodes in a network, based on the number and quality of their connections.
- Similarity analysis: the similarity or relatedness of nodes in a network, based on their attributes or shared connections.
- Community analysis: the community or group structure of a network, based on the density or modularity of the connections.
- Link prediction: the probability of shared links between entities, based on the their attributes and mutual connections.
Graph analytics is a powerful and versatile technique that can help you analyze and understand the complex relationships in your data. By using graph analytics, you can uncover hidden patterns and insights that can inform your decisions and actions. Graph analytics can be applied to various domains and use cases, such as social network analysis, fraud detection, recommendation systems, supply chain optimization, and more.
Graph analytics requires specialized tools and algorithms that can handle the complexity and scale of graph data, such as graph databases, graph visualization, and graph algorithms. Graph analytics is an emerging and fast-growing field that offers many opportunities and challenges for businesses and researchers.
Graph Analytics Market by Component, Deployment Mode, Organization Size, Application (Route Optimization and Fraud Detection), Vertical (Healthcare and Life Sciences, Transportation and Logistics, and BFSI), and Region - Global Forecast to 2024]: [Graph Analytics Market Size, Share, Growth, Trends, Analysis Report, 2020-2030]
What is Graph Data Analytics?
Graph data analytics is an emerging form of data analysis that helps organisations understand and leverage the complex relationships between linked entity data in a network or graph. Graph data analytics can be applied to various domains and use cases, such as social network analysis, fraud detection, predictive policing, supply chain optimization, and more.
Graph data analytics can help answer questions such as:
- Who are the key players or influencers in a network?
- How are the nodes in a network related or similar to each other?
- How can the network be divided or grouped into meaningful segments or communities?
- What are the patterns or trends in the network over time or space?
- What are the outliers or anomalies in the network that require attention or investigation?
Why is Graph Analytics Important for Big Data?
Graph analytics is important because it can handle the complexity and scale of big data, which often involves large, diverse, and dynamic datasets that are interconnected and interdependent. Analysts have to search through many different systems, then try to integrate the data manually, every time. Consider that intelligence analysts going through a 32GB iPhone may have to dig through 7-8GBs of data. This is time consuming, error-prone, and may miss important connections.
Graph analytics can help:
- Integrate and harmonize data from multiple sources and formats, such as structured, unstructured, or semi-structured data.
- Analyze data in real time, enabling faster and more accurate decision making and action taking.
- Incorporate artificial intelligence and machine learning techniques, such as natural language processing, computer vision, or deep learning, to enhance the analysis and generate new insights.
How do Graph Databases and Graph Analysis Work Together?
Graph databases are not new - they’ve been around since the 1960s, and have been used for applications such as social networks, recommendation engines, fraud detection, and knowledge graphs. But they have gained popularity in recent years, thanks to the rise of big data, artificial intelligence, and the internet of things. One of the main advantages of graph databases is that they can handle highly connected data, which is becoming more common and valuable in the digital age.
Graph databases began entering workflows in the mid-1980s, as an alternative to traditional databases, and are commonly referred to as a NoSQL – a direct response to the SQL relational databases that arose in the ‘70s. They use graph structures for semantic queries with nodes, edges, and properties to represent and store data. They allow users to create, query, and manipulate graphs using graph models, such as property graphs or RDF graphs. Graph databases offer several advantages over traditional relational databases, such as:
- They can represent complex and heterogeneous data more naturally and intuitively, without requiring rigid schemas or tables.
- They can perform faster and more efficient queries, especially for traversing or joining data across multiple relationships, without requiring costly joins or indexes.
- They can scale horizontally and vertically, supporting large and distributed graphs, without compromising performance or consistency.
Graph databases provide the foundation for graph analytics, as they enable users to apply various graph algorithms and techniques to the graph data. Some of the common types of graph analytics algorithms are:
Centrality algorithms: These algorithms measure the importance or influence of nodes in a graph based on their connectivity and position within the graph. They can help find the key players in a criminal network, or the bottlenecks or bridges in a graph. Community detection algorithms: These algorithms find groups or clusters of nodes in a graph that have more connections among themselves than with other nodes. They can help identify communities or subgroups of people with similar behaviours, characteristics, or regions or areas with similar properties or features. Similarity algorithms: These algorithms compare nodes in a graph based on some measures of similarity or distance, such as the number of common contacts, the length of the shortest path, or the angle or correlation between the nodes. They can help find patterns or trends in the data, for example possible criminal connections. Link prediction algorithms: These algorithms estimate the probability or likelihood of a link or edge forming between two nodes in a graph. They can help predict future interactions or relationships between nodes, such as identifying possible cyber or real-world attacks.
Relational databases are still relevant in managing data, yet are best-suited to keeping data transactions secure and promoting a high level of data integrity. Unfortunately, the very constraints that allow these benefits are the very reason they tend to struggle to adapt to domains with complex connected data.
With the data we collect becoming increasingly interconnected, graph databases offer infinite possibilities due to their flexibility, which can be leveraged as project or organization’s requirements change on-the-fly.
The Graph Analytics Market Size
The Graph Analytics Market was USD 1.14 billion in 2022 and is predicted to grow to USD 7.29 billion by 2030, with a CAGR of 33.82% during the forecast period. The major factors driving the growth of the graph analytics market are the growing volumes of massive siloed datapools, the growing adoption of cloud-based services, and the rising need for data governance and security.
Example of Graph Analytics Use Cases
One of the leading examples of graph analytics in action is GraphAware’s Hume, a user-friendly and efficient graph analytics solution. Hume can help you connect and integrate data from various sources and formats, creating a unified and enriched graph that represents your domain of interest. Exploring and analyzing the graph data using a rich set of graph algorithms and visualizations, you can discover new insights and patterns that can inform your decisions and actions. You can also automate workflows, creating alerts, reports, or dashboards that can monitor and communicate the results and outcomes.
Hume can be applied to various domains and use cases, such as:
- Law enforcement and intelligence: graph analytics can help you connect and analyze disparate data sources, such as crime records, social media, or geospatial data, creating a single source of truth for your investigations. You can also leverage spatial and temporal analysis, natural language processing, and graph visualization to improve your situational awareness and intelligence, enabling you to protect your community and prevent threats.
- Financial fraud detection: graph analytics can help you identify and prevent financial fraud by finding suspicious patterns and alerts in the graph, such as circular transactions, shell companies, or unusual behaviors. They can also help you visually explore all the involved entities and their relationships, enabling you to understand the context and the impact of the fraud.
- Life sciences: graph analytics can help navigate and understand the complex and ever-growing data available to researchers, such as genomic, clinical, or drug data. You can also use state-of-the-art data science and highly configurable Knowledge Graphs to map and analyze complex problems, such as disease diagnosis, drug discovery, or personalized medicine.
How do Graph Data Analytics Help Intelligence Agencies and Police?
Graph data analytics is a vital tool for law enforcement and intelligence agencies, as it enables them to analyze vast amounts of data and uncover hidden connections that can aid in investigations. For example, graph data analytics can help them:
- Analysts may know that all data items are connected, but can’t simply relate them, even when they come from the same source. Hume, for example, exploits knowledge graphs to naturally store implicit and explicit connections among items coming from the same or multiple data sources.
- Criminal intelligence requires searching across multiple heterogenous data sources and finding matches to support evidence. The knowledge graph model acts as a uniform single source of intelligence overcoming the heterogeneity of the original data sources.
- Considering the amount of data just a single each device (phone, tablet, pc, etc.) contain, and, in general, the amount of data accessible for each investigation (traffic videos and images, cellular towers, digital reports, etc.), it isn’t realistic to expect officers or police staff always to do it manually. Hume Orchestra provides the tools to read data from multiple sources and organise in a single or multiple graph, to solve cases from financial fraud to uncovering terrorist networks.
- For a single case, analysts have to search across multiple Excel files and manually identify connections. This takes time and effort without assuring good and reliable results. Visualization allows analysts to find relevant information across all available data sources and bring them to the canvas where it is easy to spot relationships among entities. In this way, identifying potential suspects or persons of interest in high-volume crime, such as theft, burglary, or robbery, is made simpler by finding nodes that have similar or frequent relationships with the crime scenes or victims.
By using graph data analytics, law enforcement and intelligence agencies can enhance their capabilities and performance, solving complex and challenging cases faster and more effectively.