Analytics is generally used to gain insights from numeric data. However, graph analytics analyzes relationships between entities rather than numeric data.
Using graph algorithms and relationships in graph databases, graph analytics solutions uncover insights in fields like social network analysis, fraud detection, supply chain, and search engine optimization. Explore graph analytics, types, and tools with use cases!
Types of Graph Analytics
- Centrality analysis: Centrality measures identify the graph’s most important or influential nodes. Examples include degree centrality, closeness centrality, and betweenness centrality, commonly used in social network analysis and influencer detection.
- Graph Traversal: Traversal algorithms, such as Depth-First Search (DFS) and Breadth-First Search (BFS), are used to explore all nodes in a graph and identify patterns or anomalies. It is particularly relevant in AI, pathfinding, and network diagnostics.
- Community detection: The distance and density of relationships can be used to identify groups of people who frequently interact with each other in a social network. Community analytics also deals with the detection and behavior patterns of communities.
- Connectivity analysis: Determine how strongly or weakly connected two nodes are.
- Path analysis involves finding the shortest, longest, or most optimal paths between nodes in a graph. It is often used in routing algorithms, supply chain logistics, and network optimizations.
Top 6 Use Cases for Graph Analytics
Graph analytics applications are utilized in various industries, including journalism, telecommunications, social networks, finance, and operations.
1. Journalism
A now-classic example of using graph analytics to identify networks of relationships is the International Consortium of Investigative Journalists (ICIJ) ‘s research on the Panama Papers. This research shed light on how authoritarian leaders and politicians used complex sets of shell companies to obscure their wealth from the public.
Armed with graph analytics and document extraction tools, journalists were able to get structured data from thousands of documents on companies in offshore jurisdictions and use graph analytics to navigate through the structured data in the documents to identify the real owners of these companies.
2. Compliance
Graph analytics are used to spot frauds or criminals and unlawful actions such as money laundering and payments to sanctioned entities. To detect criminals, analysts use the data of social media, texting, phone calls and emails to create a graph that shows how these data are related to criminals’ records. With that graph, government agencies can identify threats from non-obvious patterns of relationships.
- Financial transactions form graphs and can be analyzed for compliance reasons, for example. Banks need to ensure that their customers are not in any way connected to sanctioned entities.
- Loan decisions can be made using social or financial networks.
3. National security
Though controversial, graph analytics is being used by national intelligence agencies to detect unlawful activity. Communication activities of both suspected and non-suspected individuals are collected and analyzed to identify non-obvious relationships and identify potential crimes.
4. Operations
Compliance
Financial entities are required to prevent payments to sanctioned entities and graph analytics are used to spot such payments.
Fraud detection
In businesses that work with networks, such as telecom companies, e-commerce marketplaces, or financial institutions, graph analytics is used in fraud detection.
In addition to traditional graph analytics platforms, cloud-based AI services are increasingly being used to extract insights from graph-structured data. For instance, Google Vertex AI enables teams to build and deploy machine learning models that incorporate graph-based features, such as node embeddings or relationship scores.
Supply Chain Optimization
Graph analytics algorithms, such as shortest path and partitioning, are tools to optimize routes in airlines, transportation networks, and supply chain networks.
Utility optimization
Companies that provide utilities such as water, sewage services, electricity, dams, and natural gas can leverage graph analysis to build the most optimal utility distribution network.
5. Marketing
Social Network Analysis
Social media networks such as Instagram, Spotify, and LinkedIn are relationship and connection-driven applications. Graph analytics helps identify influencers and communities in social media networks. Social network influencer marketing is an emerging trend, driven by the growing number of social media users and increasing customer skepticism towards more traditional forms of marketing.
Recommendation engines
You may have noticed social networks suggesting “People you may know” or “Songs you may like.” These recommendations rely on collaborative filtering, which is a method commonly used by recommendation engines. Collaborative filtering relies on graph analytics to identify similar users, enabling personalized recommendations.
Technology companies that are not social networks also rely on collaborative filtering. For example, eBay provides the most relevant search results according to purchase history.
6. Healthcare
Pandemic Search
The world is facing a pandemic of COVID-19. Since the virus is known as highly infectious, using a graph database help governments track the spread of the virus. A company called We-Yun has developed an application using the Neo4j graph database that enables Chinese citizens to check if they have come into contact with a known carrier of the virus. The image below is a screenshot of the application, which displays all known cases connected to the name.
What are the leading graph database software tools?
Graph database tools are required for advanced graph analytics. Graph databases connect nodes (representing customers, companies, or any other entity.) and create relationships (edges) in the form of graphs that users can query. Some of the leading graph database software tools are:
- Neo4j: A popular graph database, Neo4j excels in handling large datasets and performing complex, real-time analytics.
- TigerGraph: Known for speed and scalability, it’s good for handling massive graphs and is commonly used in large enterprises.
- Amazon Neptune: A fully managed graph database from AWS, supporting both property and RDF graphs.
- Microsoft Azure Cosmos DB: A multi-model database service with graph database capabilities, supporting real-time data access.
- GraphX (Apache Spark): A part of the Apache Spark ecosystem, GraphX is optimized for large-scale graph computations, making it particularly beneficial for big data environments.
- JanusGraph: An open-source, distributed graph database that scales well for large graphs.
- Linkurious: A tool for visualizing complex graph data, commonly used in fraud detection.
- Gephi: An open-source tool for data scientists, offering powerful graph visualization capabilities.
- ArangoDB: A multi-model database with graph, document, and key-value store support, suitable for versatile applications.
For example, Neo4j is available both as an open-source solution and through a commercial license for enterprises.
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.