Graph Technologies – Why is Graph Technology a Critical Enabler For Future Innovation?


AuthorATMECS Content Team
Published Date: 15th Sep 2022 | Estimated Read Time: 4Min

 

Graph Technologies are one of the trending technologies nowadays to help analyze vast amounts of information. To understand why this is so, it may be useful to first understand what a graph is? A graph (or more commonly known as a network diagram) is simply a set of objects called nodes with interconnections called edges. And, why would one want/care to study graphs? Because they are everywhere. From a company’s internal email/chat data to complicated stock market trends, from social networks to information networks or even biological networks, graphs are ubiquitous. This is why gaining expertise in graph technology  can set your company apart from competition. 


Fig: Network of Flights from The Bio Diaspora Project

All evolving and established companies nowadays pay high salaries for graph analytics practitioners to help with their businesses and their clients. Graph technologies have different business aspects/challenges considered each time, making them a much sought field of expertise. Discerning relationships and interconnections we thought never existed now can be studied using graph technologies. Covid-19 proved that graph technologies to understand contact tracing were going to be very important to the future of technology. Digital marketers are breaking ground into behavioral analytics by studying the types of websites one visits in a given day through graphs. It is probably safe to surmise graph technology, while still in its nascent stages, can be guaranteed to be one of the top analyzing techniques in the upcoming decades.

Graph Technologies and all you need to know about them

Graph Technology is one of the most up-and-coming analytical technologies. It is often noticed that traditional graph analytics are not able to comprehend or discern patterns as the complexity and scale of today’s networks grows rapidly. Hence, the emergence of advanced graph technologies. Graphs aid in the visualization of data and maximize the understanding of the network relationships concepts. Since networks are easy to visually comprehend, the empirical observations of relationships or interconnections becomes straight forward.


Fig: Human Mobility Network Derived from Bank Note Fluxes

Graph Technology helps organizations with a new and effective way of processing, managing, and storing enormous amounts of data. It is an innovative approach leading to timely insights helping grow businesses. For ex: Think of studying a network of people you get emails from and ones to respond to in a given day. Extrapolating the idea across the organization, can help HR discern who the power centers are or who the next (hidden) leaders are in an organization. Imagine doing a similar study if you work in the travel desk of the organization. Understanding patterns in business travel with graph technology can save an organization millions of dollars every year. For deeper understanding, graph technologies can be divided into three sections. They are – graph theory, graph analytics, and graph databases.

  • Graph Theory
    Herein the graphs are drawn up and used to connect different paths and links of the objects and their interlinked relationships. Almost everything can be studied through graph patterns and understood instantly. Graph theory is a prominent part of the process as it lays the foundation for the whole procedure to be carried out further.
  • Graph Analytics
    Issues arising in different subjects can be resolved by observing the general trends of the graphs and predicting the upcoming course of the concerned area. One of the most common uses of such graph technologies can be seen in the stock market. If you are into speculation trading, understanding false positives and for that matter, even false negatives, can make you quite lucrative if you are an expert in graph analytics.
  • Graph Databases
    Graph databases allow people to store the results produced after the process of graph analytics is completed. Previously held data can be compiled in the same database to be easily accessible afterward. Data collection is one of the most prevalent examples of graph databases.

Few leading graph analytics tools and databases include but are not limited to: Amazon Neptune, IBM Graph,    Neo4J (this author recommends), Oracle spatial and graph, DGraph, Data Stax, Cambridge Semantics  Anzograph etc.

Why will developers and analytics practitioners prefer Graph Technologies?

Graph technologies have started growing in the past couple of years, but the real question is – Are graph technologies worth the hype?

  • Traditional analytics are based on concepts with long codes and hours of programs whose results are promising and accurate but time-consuming.
  • It has been observed that while a specific amount of data can take up to 1000-4000 lines of code, it can be overcome easily by completing the task in less than 400 code lines in Graph analytics.
  • Ease of learning, ease of understanding and use, ability to scale, ability to handle complexity are all compelling reasons why graph technologies have now become very attractive. As cloud computing matures, we will see more practitioners wanting to innovate in the graph technology space.
  • Graph technologies have use cases across industry domains as networks exist virtually everywhere. Gaining expertise in graphing technologies will ensure an exciting career path.

Graph technologies are expected to be extremely promising in the upcoming decade. They are considered one of the significant strides needed for advancement in technology. Different sectors, like health, business, banking, agriculture, logistics, utilities etc., are looking for its applications in their respective fields.

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