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August 26, 2020

Graph Analytics - The Science of Network Analysis

Gartner predicts that the adoption of graph analytics and graph databases will grow multifold at 100% annually through 2022 to continuously accelerate decision making and enable more complex and adaptive data science. By 2023, graph technologies will facilitate rapid decision making in 30% of organizations worldwide.

Through this blog, we will understand What is Graph Analytics, Types of Graph Analytics, and a few Use Cases.

A.    What is Graph Analytics

Graph represents relationship between two entities. Graphs are made up of Nodes or Vertices (entities) & Edges or Links (relationships). The graphs provide a scalable and supple platform for finding connections between data nodes or analyzing the data derived from strength of relationships. Graph Analytics is the methodology of analyzing relationship amongst entities to uncover insights that are difficult to visualize with other techniques. This analysis is supported by building graph algorithms powered by graph databases.

·         Graph analysis enables how several trends or data entities are related to each other

·         Graph models determine connectedness across data points to determine the nodes that generate the most activity or sphere influence

·         Graph databases provide expanded capabilities for storing, calibrating, and analyzing graph models

·         Generating dynamic graphs instead of static relational schemes help uncover deeper insights

B.      Types of Graphs 

·         Path Analysis: Analysis to determine the relationship and shortest path between 2 nodes. A typical applicability includes route optimization in supply chain and logistics

·         Connectivity Analysis: Analyze the strength or weakness of connection between two nodes, can be applied to identify weaknesses in networks

·         Centrality Analysis: Analyze how important a node is for connectivity of the network, can be used to identify most influential people in a social media network or to find high traffic web pages

·         Community Detection: Distance and/or density of relationships can be used to identify communities and detection and behavior patterns

C.       Relevant Use Cases

·         Detecting financial crimes like money laundering or payments to prohibited entities

·         Identifying fraud in banking transactions, fraudulent insurance claims, and suspicious activities in telecommunications

·         Regulatory compliance - tracking sensitive data lineage through enterprise systems

·         Graph algorithms can optimize airline routes, supply distribution chains, and logistics

·         Support study in Life Sciences - medical investigation, vaccine development, disease pathology

·         Social Media - identify social influencers and online communities

·         Optimization of recommendation engines in eCommerce platform by use of collaborative filtering resulting in more personalized recommendations

Leading Graph Database Tools

                Amazon Neptune, Neo4J, ArangoDB, DataStax, OrientDB, Titan

Role of Data Testing in Graph Analytics

Data Validation becomes a vital cog in successful implementation of graph processing in various domains and industries. Data (Sedentary and Streaming) from traditional, cloud, multi-cloud systems is extracted, integrated and loaded into schema free Graph DBs. Robust data validation techniques will ensure data integrity and no data loss as data moves through disparate systems. Analytics models built on top of Graph DBs also not to be validated to develop human-machine trust and a thorough Data Visualization testing needs to be conducted to ensure analysts are able to make decisions on accurate data visualization.


As data and devices become increasingly interconnected and sophisticated, it is indispensable to relationships within data and derive insights from the data resulting in faster decision making by supporting analysts. Graph are suited to this due to their exceptional capability of finding revealing patterns in the connected data. As organization and enterprises continue leveraging the power of big data and data analytics, the unique capabilities of graphs are essential to today's wants and tomorrow's triumphs.