Graphs for Data Science
Graph data science explores relationships between data points for more effective models. Neo4j GDS offers standard algorithms to solve common data science problems with graphs.
In the past, data science and machine learning applications have primarily utilized columnar data to gain knowledge and understanding. This approach treats each row of data as independent of the others.
Graph databases are an excellent choice for data science when dealing with highly interconnected data, such as social networks, recommendation systems, fraud detection, supply chain analysis, and more. Unlike traditional relational databases, graph databases store data in a graph structure, consisting of nodes (entities) connected by edges (relationships).
Graph databases excel at representing and managing relationships between data points. Graph databases can handle complex, semi-structured, and unstructured data without imposing rigid schemas. When it comes to traversing and querying relationships, graph databases offer efficient performance. As the data grows, graph databases can scale horizontally by distributing data across multiple nodes in a cluster. Many graph databases come equipped with built-in graph algorithms, such as shortest path algorithms, centrality measures, and community detection. Graph databases enable advanced pattern matching, facilitating the discovery of hidden patterns and making personalized recommendations based on user behavior.
Popular Graph Databases:
Neo4j: One of the most well-known and mature graph databases, Neo4j offers high performance, scalability, and a user-friendly query language called Cypher.
Amazon Neptune: A fully-managed graph database service provided by AWS, suitable for applications requiring high availability and scalability.
JanusGraph: An open-source, distributed graph database that allows users to choose between various storage backends (e.g., Apache Cassandra, HBase) for scalability.
OrientDB: A multi-model database that supports graph, document, and key-value data models, offering flexibility for various data science use cases.
ArangoDB: A multi-model database that combines graph, document, and key-value stores, providing data scientists with multiple data models in a single database.
When choosing a graph database for data science, consider factors such as the size and complexity of your data, the need for real-time analysis, scalability requirements, and the availability of relevant graph algorithms and query languages.
However, exploring the relationships between each data point can lead to more effective models in certain cases, such as social network analysis, recommender systems, fraud detection, search, and question-answering bots.
Here is a guide aims to familiarize you with some of the standard algorithms used in graph data science and show you how to apply them using Neo4j Graph Data Science (GDS) to solve common data science problems with graphs.
Neo4j Graph Data Science Library