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.
What is Graph Theory?
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.
Big data analytics has always been a fundamental approach for companies to become a competing edge and accomplish their aims.Click to explore about our, 10 Latest Trends in Big Data Analytics
How is Graph Analytics different than regular analytics?
Regular analytics based on statistics, computer programming, and operations exploration to uncover insights into data.
Graph analytics applies graph-specific algorithms to identify patterns, relationships, strengths, and weaknesses in relationships between entities or nodes. Clustering (Cluster the nodes and create various groups based on edge weights or edge distances), Partitioning (minimization of the group of adjacent nodes or edges that are not placed on the same partition), PageRank ( importance of each node within the graph, based on the incoming number relationships and the importance of the corresponding source nodes) and Shortest path (find all pairs of shortest paths from a weighted graph) algorithms are unique to graph analytics.
Also, regular analytics uses SQL for retrieving appropriate data and finding reasons behind data insights. But in SQL, have lots of SQL joins between all tables for interconnecting related queries. SQL joins are slow because there are a bunch of joins in a lot more tabular tables. In this time, the SQL queries might take thousands of milliseconds, but in the graph database, all nodes or objects are connected by relationships, and results show fast. Here the graph database takes less time (microseconds) than SQL queries because the graph database is set up to handle those joins and to navigate relationships that how databases are stored.
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.
Top 5 Use Cases of Graph Analytics are listed below:
Fraud Identification and Analysis
Social Network Analysis
Money Laundering and Financial Fraud
Finding Bot Accounts in Social Networks
All the Use Cases are described below in detail:
Today social media is a rising part of our world, and relationships are a crucial part of it. Graph databases traverse social networks and related data very quickly. Most businesses use social media for marketing their products and services—paid advertisements and designing viral content to gain an online following. Social network analysis is helpful to screen nonsensical insights that can quickly the decision-making process and identify trendsetters and social influencers who can influence the workforce to adopt beginnings.
Sometimes a fraud detection system may flag any client for behavior as suspicious. The client may be part of a larger criminal ring, or behavior may be part of a bigger scheme. This requires exploring what the client is connected to and check if they are connected with any fraud or criminal case directly or indirectly. After gathering information, Graph analytics facilities dynamically explore the relationships and powerfully connect with large datasets. Using Graph algorithms, understanding the connection across information such as an address, phone number, email, etc., and detecting associates become faster.
Optimizing the use of system resources and maximizing utilization in communication networks requires balancing loads. Analyzing the network relationships, identifying overloaded resources and reducing risk in traffic, and reconfiguring the topology to improve operations. Collecting information on concepts, entities, and relationships helps provide a framework for data integration and unification and create a large Knowledge Graph.
A knowledge graph is also a semantic network that represents real-world entities-i.e., Objects, events, situations, or concepts and describes the relationship between them. This information is stored in a graph database and visualized by a graph structure. A knowledge graph converts data into knowledge that machines can understand.
Google search engine helps users discover information faster and efficiently through a knowledge graph. The knowledge graph has millions of entries that describe real-world entities like people, places, things, etc.
Logistics companies use graph analytics to optimize routes and help identify the shortest routes, which can render cost-saving and ensure an effective supply chain.
By graph, technology takes advantage to unfold new possibilities in the financial services industry. Financial criminals, locations, bank accounts, and every relationship are shown in the graph network.
In money laundering problems, dirty money circulates to mix with legitimate funds then transformed into durable assets. A criminal sends a large amount of money to himself/herself but tries to hide transfer details of synthetic identities by stealing email, address, etc. Graph analytics create a graph from transactions between entities as well as sharing all information and find or identify the nodes by understanding the relationship and transactions of nodes, the direction of edges, and finding the false or duplicate accounts with similar information. Sometimes in real-time, payment services and apps try to deliver money as quickly as possible to valid users. But the user does receive the money. Graph analysts investigate the whole transaction and try to understand the heterogeneous surrounding information, especially online banking and ATM locations, to detect fraud-based datasets.
In real-world events in healthcare can be designed and create unique relationships between physicians, healthcare providers, and vendors. It gives us a unique perspective on handling patient care, money flow, and organization hierarchies. Referral networks allow seeing the flow of patients between physicians. But in reality, this identifies other social and professional relationships that physicians may share. Using graph analytics, after showing all structures in a health organization, graph analyst attempts to reduce patient and organization revenue problems by reducing patient wait times, scheduling, and treatments and predicting which patients are at most significant risk.
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.