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“ Graph Analytics enable the development of a “Digital Brain” at scale to connect the dots across and allow us to see the whole story as and when they unfold in Real-Time to make predictions and recommendations.”

What is Graph Analytics?

Graph analytics is an emerging form of data analysis that helps businesses understand complex relationships between linked entity data in a network or graph.




















A graph consists of nodes or vertices (representing the entities in the system) that are connected by edges (representing relationships between those entities). Working with graphs is a function of navigating edges and nodes to discover and understand complex relationships and/or optimize paths between linked data in a network.



Graph + Data Science = Graph Data Science

Graph data science is the study of graph-structured data, which consists of nodes (representing entities) and edges (representing relationships between those entities). It involves analyzing, modeling, and querying complex interconnected data sets. Graph data science can be applied to a wide range of fields, such as social network analysis, recommendation systems, cybersecurity, and bioinformatics.

Graph data science algorithms can be classified into two main categories: graph analytics and graph mining. Graph analytics includes algorithms such as centrality measures (e.g., degree centrality, betweenness centrality), community detection, and graph clustering. These algorithms are used to gain insights into the structure of a graph and identify important nodes and communities within it.

On the other hand, graph mining algorithms are used to extract patterns and knowledge from the graph data. These algorithms include subgraph mining, graph classification, and graph neural networks. They are used to predict properties of nodes and edges in the graph, such as labels or attributes.

Overall, graph data science provides a powerful set of tools for analyzing and understanding complex interconnected data sets, and has applications in a wide range of fields.

How does graph ingest and output data for analysis?

Graph analytics can ingest data from a variety of sources, including structured and unstructured data, social media, and cloud-based storage systems. Once the data has been ingested, graph analytics algorithms can be applied to gain insights into the structure of the graph, such as identifying important nodes and communities within it.

The output from graph analytics algorithms can then be sent to a variety of endpoints, such as chatbots, mobile apps, graph accelerators, dashboards, and data visualizers. For example, the results of a community detection algorithm could be sent to a chatbot, which could then provide recommendations to users based on the communities they belong to. Similarly, the output of a centrality measure could be sent to a mobile app or dashboard, allowing users to visualize the most important nodes in the graph.


Overall, graph analytics provides a flexible and powerful way to analyze complex interconnected data sets, and can be integrated with a wide range of other tools and applications to provide valuable insights to users.

Who can use Graph Analytics?






Graph analytics can be used by different types of users with varying levels of technical expertise, from data science users to super users and business users. Data science users typically have a strong background in programming and statistics, and may use graph analytics to build complex models and perform advanced analytics on large and complex data sets.

Super users, on the other hand, may have some technical expertise, but may not have the same level of expertise as data science users. They may use graph analytics tools to perform more basic analyses and gain insights into the structure of the graph.

Business users, who may have little to no technical expertise, can also use graph analytics tools to gain insights into the data. They may use low-code or no-code interfaces to interact with the graph, perform simple analyses, and generate reports or visualizations. User-friendly interfaces can make it easier for business users to access and understand the insights provided by graph analytics, allowing them to make more informed decisions.

Overall, graph analytics can be used by a wide range of users with varying levels of technical expertise, and can be adapted to suit the needs of different user groups. This flexibility makes it a valuable tool for organizations looking to gain insights from their data and make data-driven decisions.


Starting your Graph Journey

BioQuest Advisory offers a comprehensive and tailored approach to graph analytics that provides clients with the industry knowledge and expertise needed to create meaningful insights from their data. We partner with clients to identify use cases and design a graph data model that is specific to their needs, and assist with data ingestion and analytics to gain insights into the structure of the graph.


Our team can also help to build custom dashboards and visualizations, while training the users to leverage the full potential of the graph data model. We work closely with clients, collaborating and sharing insights, to ensure they are engaged throughout the process and can derive maximum value from the insights generated by the graph analytics.

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