Graph analytics is an emerging form of data analysis that helps analyze data in graph form. A graph is a collection of nodes that are connected by edges. In the real world, the nodes can be objects, such as webpages, persons, places, devices, airports, etc. The edges are the relationship between the nodes, such as the number of times a webpage is accessed, likes or dislikes, payments made, or phone calls received, among others. In graph analytics, the data points are the nodes and their relationships are the edges. Graph databases play a vital role in storing data in the form of tables, key-value pairs, or document-oriented databases. Graph analytics uses graph algorithms and the relationships in the graph database between the nodes. The direction and weights between the nodes indicate the strength of the relationship.
How Does Graph Analytics Work?
Graph analytics uses specific algorithms such as clustering, partitioning, shortest path, and PageRank to analyze the relationships between nodes. Graph database tools are required for graph analytics to connect nodes and establish their relationships in the form of graphs. They can be queried using API and cypher language. Graph data models have the flexibility to add a new node without altering the existing structure. Connections between nodes are direct, enabling multiple node hops in seconds. By avoiding joins and lookups, the response time becomes shorter than in the traditional RDBMS database.
The key types of graph analytics are centrality analysis, community detection, connectivity analysis, and path analysis. The following diagram illustrates a couple of queries with an example of working with graph databases using cypher language.
Use Cases of Graph Analytics
Graph analytics finds application in various sectors, including supply chain, telecom, health, e-commerce, and fraud detection. Here are some examples of how it can help:
1. Search engines, such as Google, use knowledge graphs to improve recommendations based on users’ search history patterns.
2. The transportation industry leverages graph analytics to determine the fastest and safest route between two points.
3. The banking sector makes use of graph analytics to identify illegal or criminal activities and assist in making loan sanction decisions.
Neo4j, OrientDB, Amazon Neptune, ArangoDB, Cayley, and Titan are some of the graph databases available. Oracle’s Spatial and Graph, Apache’s Gremlin, Microsoft’s Graph Engine, Amazon’s Neptune, SAP’s HANA, and IBM’s Compose for JanusGraph are among the key players in the market. Microsoft recently enhanced its data connect functionality that combines analytics data from Microsoft Graph with customer data. AWS announced new features, including Amazon QuickSight, to help organize assets and send email alerts on anomalies. IBM’s new API Kit enables access to data stored on-premises or in the cloud.
Advantages and limitations of graph analytics: While graph analytics provides flexibility and agility at a low cost, it is not the best option for business intelligence (BI) or performance analytics. Its enhanced data retrieval performance offers nth level interconnected hierarchy. However, it is limited by its inability to support dataset versioning and maintain audit trails of dataset updates. Graph analytics does not make use of standard query language.
Market trends
The graph analytics market is expected to grow to USD 2.1 billon by 2027, up from USD 583 million in 2020. The growth will be most significant in health care, retail and supply chain, as well as fraud detection. It will primarily help analyze low-latency queries and real-time data flow.
In e-commerce, graph analytics plays a vital role in providing accurate recommendations for products. While traditional analytics may make two or three hops to offer suitable recommendations, graph analytics traverses n hops to gather relevant data for more accurate recommendations. Its nth level interconnected hierarchy helps it to better understand customer needs, intent, and interests for precise analysis. With exponential growth in data velocity, graph analytics is expected to play a significant role in big data technology. Where regular analytics can prove to be a bottleneck, graph algorithms can help analyze huge volumes of data.
With potential applications in data modernization, master data management (MDM), data quality management (DQM), metadata management, and other areas of artificial intelligence (AI), more organizations are adopting graph analytics. This offers opportunities in browser combability testing and data testing in MDM and DQM, along with BI data visualization. Where manual effort to validate data and graphs would be high, automation reduces cycle time and increases data accuracy. For example, validating graph data by comparing two images is faster than comparing data queried from a database against a report. With the market demonstrating the potential for native automation solutions, Infosys has developed cutting-edge tools and accelerators to test the graph analytics landscape.
Conclusion
Graph analytics is an emerging technology which is useful for unstructured, real-time, and variable datasets. Adept at drawing data inferences, it helps enhance the accuracy of predictions and decision-making over traditional methods. However, in order to handle large volumes of data on a single server, it must improve its parallel processing algorithms. Devising complex algorithms in practice can help it manage all scenarios presently covered by traditional databases.
