By 2025, Graph Technologies will be used in 80% of data and analytics innovations. Graph technologies is gaining importance disrupting the world of Artificial Intelligence, by solving some of the key challenges in areas of Financial Crime, Supply Chain management, Knowledge management & many more. Benefits include, better, smarter, accurate solutions with increased performance & better explainability.
SwissCognitive Guest Blogger: Vidhya Veeraraghavan – Vice President, Artificial Intelligence Delivery Lead – Financial Crime & Fraud Detection
I pause to ask, “why”? How has Graph Technology gained so much prominence in the Data Science community? How is it assisting the Artificial Intelligence landscape.
Let’s begin with a few basics.
What is Graph Technology?
To understand Graph Technology, we need to understand few interlinked concepts, such as Graph Theory, Graph Analytics, Graph Database and Knowledge Graph. Let’s see a brief overview of each of these concepts.
In mathematics, Graph Theory is the study of lines and points. It is a sub-field of mathematics which deals with graphs, diagrams that involve points and lines, which often pictorially represent mathematical truths. Graph Analytics, also known as Graph algorithms, are a set of instructions or analytical tools, that traverse a graph to determine strength and relations between the entities present in a graph, i.e. the nodes and the edges. Nodes denote entities in the graph database. Edges symbolize the relationships, or lines of communication, between nodes. These edges can be directional or non-directional. Graph Analytics (or algorithms) are applied on the graph database to uncover patterns and relationships. Graph Database commonly referred to as a NoSQL, are purpose-built databases that store data using graph structures for semantic queries to represent and navigate relationships. These are like 1970s network model databases but with better traversal over a chain of edges.
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How is Graph Technology changing AI landscape?
World is a Graph, everything is connected. Graphs capture explicit and implicit context and relationships in a single flexible model. Humans understand the world as relationships and connections. Bringing this important aspect inside the AI model, amplifies model’s learning process to solve tasks that mimics human decisions.
Graph Technology powers up Artificial Intelligence by providing Context. Let’s look at some benefits.
Predictions are more accurate
Relationships are some of the strongest predictors of behaviour. More contextual information makes AI models more predictive. By identifying and incorporating predictive relationships and network data, model accuracy is improved.
For example, we can run graph algorithms such as Centrality (eg: PageRank, Closeness), Community Detection (eg: Louvain), Similarity of neighbourhoods (eg: Jaccard) on customer data to identify group clusters or partitions, most important influencers, connectivity strengths and weaknesses, how similar are customers to each other, what products to recommend next, likelihood of customers to form or break connections, and many more insightful patterns.
Knowledge Graphs make AI solutions smarter
Unless contextual knowledge is transferred into the database, the queries may not yield the right results specific to the context. Knowledge Graphs help connect the internal and external data sources in an intelligent way which paves way to improved search, identifying Sales & growth opportunities, and better managing Supply chain and compliance risks.
Connected Feature Extraction
Graph algorithms are specifically built to operate on relationships, and they are uniquely capable of finding structures and revealing patterns in connected data. Connected feature extraction is the process of changing the shape or format of graph data so that it is usable in a machine learning pipeline. Graph Centrality algorithms such as PageRank and Closeness Centrality, come in handy for feature engineering to create new features with more predictive power that can improve accuracy of the models.
Graph-Accelerated ML increases performance
We understand that the most challenging ML task is to iteratively train the model. Most of the organizations resolve to buy more GPUs to tackle this challenge. However, that doesn’t always work because the problem statements are not similar. Table joins can be replaced using graph queries without altering the algorithms. Running Graph machine learning workflows on accelerated hardware gives better performance than other forms of techniques.
Increase in Trust with better Explainability
Graphs provide data lineage – understanding, recording, and visualizing meta data as it flows from data sources to consumption. Associating nodes in a neural network to a labelled Knowledge graph allows for traversing related documents to an explanation which also helps explain the model’s decisions in a human-friendly way.
In this article, we have traversed through the importance of Graph Technology and how it is predicted to dominate the market share in the near future. Artificial Intelligence is an evolving field of study and practice which is getting powered by connected data through Graph Technologies. The benefits of using Graph technologies in AI landscape has its true merits. We also understood some key Graph concepts such as Graph Theory, Graph Analytics (a.k.a. Graph Algorithms) and Graph Database. More organizations and practitioners are advocating to shift from traditional technologies to newer and emerging technologies such as Graph Technologies.
About the Author:
Vidhya Veeraraghavan is a Data Evangelist, Keynote Speaker and Thought Leader based in Bangalore, India. She has won many leadership awards and is well-respected in the Data community. She is a hands-on problem solver delivering AI and ML projects at all levels of business making significant strategic benefits to organizations across the globe. With her contribution and her ability to build, shape, lead, inspire, coach & mentor high-energy teams, Vidhya has proved to be an inspiration to all.