As the data volume is growing and the world is shifting towards big data, there comes a need to derive the business value from that data to get the best insights. Graph Analytics can help resolve real-world problems and provide a boost to the way businesses work in the ever-changing market. The demand is to get the complex information across internal and external data, the structured and unstructured data, and want to blend data from different applications.
The graph analytics component allows users to load data, run feature engineering, and run machine learning modes to compute the network score. Source: IBM Knowledge Center
Though data by itself has little to no value, connecting data is essential to provide context, make sense of the underlying implications of data, and for analytics to deliver value. Mainstream Query tools and languages that we have been using now, like SQL, can’t analyze this complex data level at such a massive scale.
What is Graph Analytics?
An analytics domain covers the relationship between graph database entries via an abstraction called graph model. It combines graph-theoretic, statistics, and database technology to model, store, retrieve and analyze graph-structured data. Organizations leverage graph models to gain marketing, security, finance, for example, for analyzing social networks. It is a technology that can be leveraged in different industries like fraud detection, supply chain management, SEO, etc. It helps to resolve real-world problems in unconventional ways.
Graphs are unique data structures that can model different relationships and processes over physical, biological, social, and information systems. Consisting of nodes or vertices (representing the system's entities) connected by edges (representing relationships between those entities), these are more than just nodes and edges. These are powerful data structures you can use to define complex dependencies in your data.
What are different types of Graph Analytics?
A simple graph can be as simple as you want it to be as informative as much in-depth you want to analyze it. There are some predefined analytics for the graphs, based on which we identify graph analytics as divided into four types:-
Path Analysis: In this, the relationship between nodes in a graph is analyzed. To determine the shortest distance between two nodes.
Connectivity Analysis: Over a network, graphs help compare connectivity by outlining how strongly or weakly two nodes are connected. This helps determine how many edges are flowing into a node and how many are flowing out of that node.
Centrality Analysis: This analysis estimates the importance of a node in the network's connectivity. Determine the social media influencer by ranking out the most highly accessed web pages.
Community Analysis / Network Analysis: This is a distance and density-based analysis of relationships used upon people to analyze and find the groups of people frequently interacting with each other in a social network. This also helps identify whether individuals are transient and predicts if the network will grow.
Using graph analytics, applications employ algorithms that traverse and analyze graphs detecting and potentially identifying interesting patterns symbolic to business opportunities. For performing Graph Analyses, there are to be chosen some graph algorithms or some models, which can be implemented to get the required result and the analysis you need to perform on the graph. Different Algorithms used in graph analytics -
Path analysis is an algorithm that helps to analyze the distances and shapes of the various paths that connect entities within the graph.
Clustering helps to examine the properties of the vertices and edges to identify the entities' characteristics that can be used to group them.
Pattern analysis and pattern detection, or methods for identifying anomalous or unexpected patterns requiring further investigation.
Probabilistic graphical models have various medical diagnoses, speech recognition, or default risk assessment for credit applications. Examples of such models are Bayesian networks and Markov networks.
Graph Analytic algorithms can detect interesting patterns that might go undetected in a data warehouse model. These patterns themselves can become the templates or models for new searches. The graph analytics approach can satisfy both the discovery and the use of patterns for analysis and reporting.
How can graph analytics be applied within different business fields?
Graph technologies have seen a specific maturity curve for their adoption by businesses. Right from the start, when the graphs help to resolve only the simple use cases applying only basic analytics to an ideal situation where companies are now looking up to using the graph data and tools on a recurrent basis. We could distinguish four different phases in this process, which mark the path towards the adoption of graphs:
Knowledge graphs for Graph Analytics
Knowledge graphs are the most popular way to represent knowledge in a semantic form in the database in a graphical manner. NLP (Natural Language Processing) can provide the relevant answer to a query in natural language. Talking mainly on how knowledge graphs are turning the game in graph analytics. For analyzing, you must know what you want to analyze. Giving the relevant answer is up to the knowledge graph. The answer will be based on the analysis performed on different sorts of data combined and represented in graphs.
So, to get the most relevant answers, you’ve got to ask the right questions, and if something helps you ask the better questions, you’re more likely to get what you need. Accumulate the pattern and build our graphs by querying a database that holds all this data instead of a graph. We would have to perform the aggregation, but here we don’t because the aggregation is inside because of the way we build the data model. To find out how many times the specific question was asked, you wouldn’t need to count all the rows for this question. That question and the number will be there when it comes to the knowledge graph. Consider it a much more accessible way of generating the most relevant queries. Knowledge graphs are the basis for useful Analytics and BI. Using these, you query databases, capture relevant searches and easily aggregate all the usage in a very easy way to analyze. Providing insights for everyone helps us access data more efficiently.
Graph analytics use cases for telecom, journalism, social networks, finance, and operations.
Graph analytics help to spot frauds and unlawful actions such as money laundering and payments to sanctioned entities. Analysts use the data of social media to detect criminals. They use texting, phone calls, and emails to create a graph that shows how these data are related to criminals’ records. Government agencies can identify the threats from non-obvious patterns of relationships from those graphs.
Graphs can be formed from financial transactions and can be used to analyze compliance reasons. For example, now banks have to ensure that their customers are not connected to the sanctioned entities.
Using social or financial networks formed over these graphs for loan decisions.
Graph analytics is being used to identify networks of relationships in the ICIJ ( International Consortium of Investigative Journalists) research on Panama Papers. The research emphasizes how authoritarian leaders and politicians used complex sets of shell companies to obscure their wealth from the public. Using graph analytics and document extraction tools to structure the data from thousands of documents on companies in off-shore jurisdictions. They used graph analytics to navigate the documents' structured data to identify those companies' real owners.
Though considered a controversial topic, national intelligence agencies detect unlawful activity using graph analytics. Online activity of both suspected and not suspected individuals are collected and analyzed to identify non-obvious relationships and identify potential crimes.
Supply Chain Optimization: In transportation networks, supply chain networks and airline companies use graph analytics algorithms such as shortest path and partitioning as tools to optimize routes.
Fraud Detection: Graph Analytics is used to detect fraud detection in businesses that work with networks involving e-commerce marketplaces, financial institutions, and telecom companies.
2020 was a pandemic year in the hands of coronavirus. Being a highly infectious virus, using a graph database helped governments track the spread of this virus. A Chinese company named We-Yun allows Chinese citizens to check if they can contact a known carrier of the virus. The application uses the Neo4j graph database.
Recommendation Engines: “People you may know” or “Songs you may like” are some common phrases you hear these days on your social media profiles. Recommendations rely on collaborative filtering, which is a commonly recommended engine. Graph Analytics helps to identify similar users and enables personalized recommendations when using this collaborative filtering.
Social Network Analysis: Social media networks such as Instagram, Linked In, and Spotify are relationships and connection-driven applications. Graph analytics has an application in identifying influencers and communities on social media.
We can yield interesting patterns that might go undetected in a data warehouse model using graph analytics. These patterns themselves can become the templates or models for new searches. In other words, a graph analytics approach helps to satisfy both the discovery and the use of patterns typically used for analysis and reporting.