Best Graph Database Visualization Tools

Lei Huang
Chief Architect & Co-Founder
April 21, 2024
Best Graph Database Visualization Tools

Are you seeking a graph database visualization tool to clarify intricate data connections? Look no further. This concise guide pinpoints the top tools that blend intuitive interfaces with advanced features for comprehensive data analysis.

Graph visualization is a helpful way to show and understand how data is connected. With interconnected data generated by our complex data pipeline every day, it's becoming increasingly important to know how the data are connected today. For example, graph visualization is beneficial when data scientists and others deal with social network analysis, biological systems, computer networks, or financial transaction data.

What is Graph Database Visualization 

From the basics, graph database visualization takes complex data and turns it into simple, easy-to-understand canvas pictures. We will discuss how different graph data models can impact the visualization. By mapping our data entities as nodes and relationships as edges, we can quickly see patterns, identify important nodes, and understand how information flows through the network.

Graph visualization can be applied intuitively in many use cases:

  • Finding the most influential people in a social network
  • Spotting groups or clusters in complex systems
  • Understanding how information or resources move through a network
  • Discovering unusual patterns or outliers in big datasets
  • Explaining complex relationships with visualized graphs

However, creating compelling graph visualizations can be challenging. There are a few key challenges we need to keep in mind:

  1. Data Model: Figuring out how to represent our data as a graph and define the relationships between entities.
  2. Visualization:some text
    1. Scalability: Making sure our visualizations can handle big and complex graphs
    2. Readability: Keeping things clear and easy to understand
    3. Aesthetics: Creating visualizations that look good and are visually appealing
  3. Interactivity: Allowing users to easily explore, navigate, and play around with graph visualization.
  4. Usability: Making it quick and simple to update and try out different visualization options.

In this blog post, we'll discuss the basic requirements of graph visualization and the most important aspects we will look for. We will also list a few graph visualization tools that can help make the process fast and easy.

How Does Graph Database Visualization Work

In this section, we will walk through the most critical aspects of graph visualization step-by-step, from the data source to the output format.

Data Source and Graph Model

First, we need to figure out where our data is coming from. The data we want to visualize could be coming from different sources and with totally different formats and access characteristics:

  • Plain files: CSV, Parquet, RDF, or spreadsheet.
  • Traditional databases: PostgreSQL or MySQL
  • Cloud data warehouses: Amazon Redshift 
  • Data lakes: Apache Iceberg, Apache Hudi
  • Graph databases and engines: PuppyGraph, Neo4j, or Amazon Neptune

Mapping your data sources and underlying data into a graph is the most critical step in graph visualization. How we represent the data as a graph can significantly impact the clarity and effectiveness of the graph visualization. We will have different challenges with different data sources. Let's talk about the details one by one.

A data visualization example using PuppyGraph 

Plain Text Files and Traditional Databases

When working with plain text files or traditional databases, the main challenge is to identify the entities and relationships from the data. More precisely, we must determine which columns or fields represent the nodes and which represent the edges to map the data into a graph.

To effectively create the mapping, we will need to understand the logical connections between the data. For example, in commercial relational databases, we may have tables for "Customers" "Orders" and "Products". In this case, you could map the customers and products as nodes and the orders as edges connecting them. However, we can also map "Orders" as nodes and map the "Orders.buyer" as the relationship between the "Orders" and "Customers". There are no best-for-all solutions to the data model; we will need to think about the overall goals of our graph visualization and decide how to represent the data better.

Large Cloud Data Warehouses or Data Lakes

Scalability becomes a more significant concern when dealing with massive data sources like cloud data warehouses or data lakes. In most cases, visualizing every single data point as a node and edge can quickly become impossible when we have billions of entities and relationships.

When dealing with massive data, we will need to prepare the data sources and make them ready for visualization.

  • Aggregation: Group related data points together and represent them as a single node. For example, instead of showing individual sales transactions, we could aggregate them by product category or customer segment.
  • Partitioning: Divide your data into smaller, manageable subsets based on relevant criteria like time, geography, or product type. Time-based partitioning is very common in data warehouses. If possible, we can choose to visualize the data from the most recent days.
  • Filtering: Remove irrelevant or low-value data points to reduce the dataset's overall size. This could involve setting thresholds for certain metrics or excluding outliers.

Graph Engines

If your data already has a graph data model with PuppyGraph or is already stored in traditional graph databases like Neo4j or Neptune, you're in luck! These systems are designed to handle graph data natively, making mapping more straightforward. You can directly query the graph database to retrieve nodes and edges based on their properties and relationships.

However, even with graph databases, you'll still need to consider which nodes and edges are most relevant to your visualization goals. You may need to apply filtering or aggregation techniques to focus on the graph's most important aspects and the connected data within it.


Once we have our data mapped into a graph, it's time to think about how we want to present it visually. The layout algorithm is the core of graph visualization. It determines how the nodes and edges are positioned on the screen.

Force Graph Layout

The most popular layout algorithm is the force graph layout. It's based on simulating physical forces between the nodes and edges. Imagine the nodes are tiny particles that attract or repel each other based on their connections. The force graph algorithm calculates these forces and finds a balance where the nodes are nicely spaced and the edges aren't too cluttered.

  • Nodes that are connected by an edge are pulled towards each other, like they're attached by a spring.
  • Nodes that aren't connected try to push each other away, like they have a magnetic repulsion.
  • The algorithm keeps adjusting the positions of the nodes until it finds a stable arrangement where the forces are balanced.

One cool thing about force graphs is that they can reveal patterns and clusters in your data. Tightly connected groups of nodes will naturally be pulled together, while loosely connected nodes will be pushed apart.

The basic force graph algorithm has many variations and extensions that can handle different types of graphs and layout requirements. Some examples are:

  • Directed force graphs that take into account the direction of the edges
  • Weighted force graphs that adjust the strength of the forces based on edge weights
  • Constrained force graphs that limit the movement of specific nodes or groups or limit the movement within a boundary (like the screen viewport)
A visualization example showing airport and flight data using PuppyGraph

Geometric Layouts

Another family of layout algorithms uses predefined geometric shapes to arrange the nodes. These can be simple shapes like circles or grids or more complex ones like spirals or donuts.

Circular layouts are great for showing cyclical patterns or relationships. The nodes are evenly spaced around a circle, and the edges are drawn as arcs connecting them.

A visualization example showing a social network platform using PuppyGraph

Grid layouts are useful when you have many nodes and want to keep things neat and organized. The nodes are placed in a regular grid pattern, and the edges are drawn as straight lines between them.

Hierarchical and Tree Layout

You might want to use a hierarchical or tree layout if your graph has a natural hierarchy or tree-like structure. These layouts arrange the nodes in layers or levels based on their depth in the hierarchy.

Tree layouts are perfect for visualizing family trees, file directories, or organizational charts. The nodes are arranged in a branching structure, with the root node at the top and the child nodes below.

Clustering Layouts

When your graph has distinct groups or communities of nodes, you can use clustering layouts to highlight these structures. Clustering algorithms like k-means or hierarchical clustering can automatically group similar nodes based on their connections or attributes.

Once the clusters are identified, you can apply different layout algorithms to each cluster separately. This can help reveal the internal structure of each group while still showing the overall relationships between clusters.

Combining Layouts with Layers

One powerful technique is to combine multiple layout algorithms using layers. You can divide your graph into subsets or categories and apply layouts to each layer.

For example, you could use a circular layout for the top-level categories and force graphs or tree layouts within each category. This can create an excellent and informative visualization that shows both the high-level structure and the detailed relationships in your data.

What Are the Types of Graph Database Visualization?

Unsurprisingly, the choice of visualization platform can significantly impact the functionality, interactivity, and ease of sharing of our visualization project. We summarized that there are three main types to output graph visualizations:

Embedded Objects

One approach is to generate static images or SVG files of our graph visualization and embed them into other systems or documents. A popular tool for this is Graphviz, which can take a graph described in the DOT language and convert it into various image formats like PDF or PNG.


  • Easy to share and embed in presentations, reports, or web pages
  • Consistent appearance across different platforms and devices
  • Suitable for static or small graphs that don't require interactivity


  • Limited interactivity and exploration capabilities
  • Requires regenerating the image every time the data or layout changes
  • It can become slow or impractical for large or complex graphs

Web-Based Visualization Tools

Another popular option is web-based visualization libraries that run directly in the browser. These tools, like D3.js, Kineviz GraphXR, or Linkurious Ogma, use JavaScript and HTML5 canvas to render interactive graph visualizations. PuppyGraph can render the graph it manages within its web UI using PixiJs as the underlying graphics engine.


  • Highly interactive and responsive, allowing users to zoom, pan, and explore the graph
  • Easy to share and access via a web browser without requiring any special software
  • Can handle more extensive and more complex graphs than static images
  • Allows for real-time updates and animations based on user interactions or data changes


  • Requires web development skills and familiarity with JavaScript and web technologies
  • Performance may be limited by the user's browser and hardware capabilities; data filtering and other preparation might be required for better performance.

Standalone Software

Lastly, there are dedicated standalone software applications that specialize in graph visualization. These tools, like gDotV, Gephi, or DOT Software, provide a GUI for importing, manipulating, and rendering graph data.


  • Offers a wide range of advanced features and customization options
  • Provides a user-friendly interface for non-technical users
  • Often includes additional analysis and metrics capabilities


  • Requires installation and setup on each user's machine
  • They may have steeper learning curves and less flexibility compared to web-based tools
  • Limited options for sharing or embedding the visualizations in other systems

Ultimately, the choice of visualization platform depends on specific needs and constraints. If we prioritize ease of sharing and interactivity, web-based tools might be the way. If we're dealing with extensive graphs or need advanced analysis features, standalone software like Gephi or Graphviz could be a better fit.

Key Factors to Consider When Adopting Graph Database Visualization Tools

Several factors should be considered when selecting a graph database visualization tool. One of the most crucial factors is the tool’s ease of use. A tool that enables users to efficiently explore relationships, discover insights, and analyze network structures within graph data enhances the overall user experience and accessibility when working with intricate data sets.

Additionally, the scalability of the tool is a significant factor to keep in mind. The ability of the tool to effectively present large datasets and maintain visual performance as the size and complexity of the data increase can greatly influence the tool’s efficiency.

Considering customization options is also a key aspect while choosing a tool. A tool that allows user interaction to personalize their visual representations according to their requirements enhances clarity and comprehension of the graph data. Also, integration capability ensures tight integration with operational systems, enhancing data quality and consistency, and enabling seamless integration with other data platforms.

Lastly, the tool’s ability to support multiple data sources enhances its effectiveness by allowing users to combine data from various sources and formats, thereby facilitating comprehensive searching, analysis, and visualization of graph data.

Top 7 Best Graph Database Visualization Tools


PuppyGraph is the only graph tool available today that can seamlessly query different SQL data sources as a graph without the need for ETL processes. This means you can say goodbye to the headache of data integration and focus on what matters: exploring and visualizing your graph data.

PuppyGraph is designed to streamline every step of the graph visualization process, from connecting to data sources and building the graph model to exploring and visualizing the graph. It's a comprehensive, user-friendly platform that caters to technical and non-technical users.

Use PuppyGraph with Multiple SQL Data Sources

One of PuppyGraph's key strengths is its ability to connect to a wide variety of data sources. Whether your data is stored in plain files, databases, or even external data warehouses, PuppyGraph has you covered.

  • Plain text files: CSV, Parquet
  • Databases: PostgreSQL, MySQL
  • Data warehouses: Snowflake, Amazon Redshift, Google BigQuery
  • Datalakes: Apache Iceberg, Apache Hudi, Deltalake
All supported data sources by PuppyGraph

The beauty of PuppyGraph's data source connectivity is that it allows you to centralize your graph visualization efforts. Instead of using different tools for different data sources, you can use PuppyGraph as a unified platform for exploring and visualizing all your data. This simplifies your workflow and enables you to combine data from multiple sources into a single graph, providing a more comprehensive and holistic view of your data.

A sample architecture with PuppyGraph

For example, imagine you have customer data stored in a PostgreSQL database, sales data in a CSV file, and product information in a Parquet file. With PuppyGraph, you can connect to all these data sources and create a unified graph that shows the relationships between customers, products, and sales. This can help you identify patterns, trends, and insights that might be hidden when looking at each data source separately.

For a step-by-step tutorial guide, please read our knowledge graph in machine learning blog that shows you how to build a graph with the Twitch user dataset.

Use PuppyGraph for Graph Data Modeling

Creating a graph data model is a crucial step in graph visualization. It involves defining how your data entities and relationships are represented as nodes and edges in the graph. PuppyGraph provides a user-friendly and intuitive interface for building graph schemas from your data sources.

Connecting PuppyGraph to your data sources automatically analyzes the structure and relationships within the data. It then presents you with a visual interface where you can map tables and fields to nodes and edges in the graph. This visual approach lets you see how your data entities are connected and helps you create a logical and meaningful graph structure.

A gif showing how PuppyGraph automatically analyzes the structure and relationships within the data

PuppyGraph also provides a command-line interface for advanced users who prefer to define the graph schema programmatically. With the command-line tool, you can write a declarative configuration file in JSON that specifies the mapping between data sources and graph elements. This can be particularly useful for complex data models or for automating the graph modeling process in a production environment.

In addition to creating the graph schema, the PuppyGraph team is also building tools for validating and testing your data model. It can help automatically check for inconsistencies, missing data, or invalid relationships in your data sources.

PuppyGraph also allows you to run sample queries against your graph model to see how the data is connected and what insights you can derive from it. This is particularly useful for exploring new datasets or for testing the performance of your graph queries. With PuppyGraph's query editor, you can write and execute graph queries using popular query languages like Cypher or Gremlin and see the results visualized in real-time.

The Gremlin query interface within PuppyGraph

Use PuppyGraph to Visualize the Graph

PuppyGraph has a powerful built-in visualization framework that can handle various graph visualization use cases. Whether you need to explore large graphs, visualize query results, or create interactive dashboards, PuppyGraph has the right tools for your use cases.

Graph Explorer

PuppyGraph Graph Explorer is designed to help you quickly navigate and explore large graphs. When you load a graph into the Graph Explorer, it intelligently loads nodes and edges gradually as you move around the canvas. Even if you're working with a graph containing millions of nodes and edges, you can still explore it smoothly with your browser.

Query Visualization Tool

In addition to the Graph Explorer, PuppyGraph also provides a powerful Query Visualization Tool. This tool allows you to visualize the results of your graph queries as interactive graph visualizations. With the Query Visualization Tool, you can write queries using popular graph query languages like Cypher or Gremlin and see the results rendered as a graph in real time.

The Query Visualization Tool offers rich features for customizing and enhancing your visualizations. You can choose from different layout algorithms to arrange the nodes and edges meaningfully, such as force-directed, circular, or hierarchical layouts. You can also combine multiple query results into a single graph, allowing you to see how different subsets of your data are connected or related.

Moreover, the Query Visualization Tool provides options for customizing the appearance of your graph elements. You can update node and edge labels, colors, sizes, and shapes based on their properties or attributes. This allows you to create visually compelling and informative visualizations that highlight your data's key insights or patterns.

Graph-Native Dashboard
The PuppyGraph Graph-Native Dashboard

For users who need to create interactive dashboards or reports with graph visualizations, PuppyGraph offers a built-in Graph-Native Dashboard feature. This feature allows you to create custom dashboards, seamlessly integrating graph visualizations with other data visualization components, such as charts, tables, or maps.

With the Graph-Native Dashboard, you can create multiple tablets, each containing different visualizations or insights. You can add graph visualizations to your dashboard by simply copy-pasting the queries from the Query Visualization Tool.

Use PuppyGraph with Other Visualization Tools

While PuppyGraph's built-in visualization framework is powerful and flexible, we understand that some users may have specific requirements or preferences for other graph visualization tools. That's why PuppyGraph is designed to be an open and interoperable platform that seamlessly integrates with various third-party visualization tools.

PuppyGraph can act as a universal graph query source for other visualization tools. This means you can use PuppyGraph to connect to your data sources, model your graph data, execute graph queries, and then feed the results into your preferred visualization tool for rendering and exploration.

Some of the popular graph visualization tools that can integrate with PuppyGraph include:

  1. GDotV: A powerful graph visualization tool that provides various layout algorithms, customization options, and interactivity features.
  2. Kineviz GraphXR: A commercial graph visualization platform that offers advanced features like geo-mapping, time-series analysis, and machine learning integration.
  3. Linkurious: A web-based graph visualization and analysis tool that provides a user-friendly interface for exploring and investigating complex graph data.

By connecting these tools to PuppyGraph, you can create a best-of-breed graph visualization solution that combines the strengths of different tools and platforms. For example, you could use PuppyGraph to integrate data from multiple sources and create a unified graph model, then use GDotV to apply advanced layout algorithms and create a visually compelling rendering of the graph, and finally use Linkurious to share the visualization with a broader audience and collaborate on graph analysis tasks.

In addition to these specific tools, PuppyGraph supports popular open-source graph query languages like Gremlin and openCypher. This means that any third-party tool that supports these query languages can also integrate with PuppyGraph out of the box.

Overall, PuppyGraph's ability to act as a graph query source for other visualization tools is a crucial strength of the platform. It enables users to create flexible and customized graph visualization solutions that can adapt to their specific needs and preferences while still leveraging the power and simplicity of PuppyGraph's data management capabilities.


Kineviz emphasizes time-based analysis and interactive visualization. It's particularly well-suited for understanding how graph relationships evolve, revealing patterns that might be missed in static representations. If your use case centers on dynamic networks, Kineviz offers specialized tools.


GdotV prioritizes speed and scalability, making it a strong contender for handling massive graph datasets.  Its rendering engine is optimized for fast visualization, even with highly complex graphs. If big data and rapid results are necessary, GdotV's performance is worth considering.


Linkurious is tailored for investigative use cases such as fraud detection, security analytics, and mapping complex relationships. It offers advanced search and filtering capabilities designed to help analysts quickly pinpoint areas of interest within intricate networks.


Graphviz is a highly flexible and customizable toolkit for graph visualization. It provides a rich set of algorithms for layout and rendering, allowing for fine-grained control over the final output.  Graphviz is a robust and well-established option if you require precise customization or integration with other systems.


Gephi offers a well-balanced blend of analysis features, an intuitive interface, and active community support. Due to its approachability and available resources, it's a popular choice for researchers, data journalists, and those relatively new to graph visualization.

Neo4j Graph Visualization Tools

Neo4j's integrated tools offer a streamlined experience for visualizing graph data stored within the Neo4j database. If your primary data resides in Neo4j, these tools provide the most convenient option for exploration and visualization without needing external transfers.


The effectiveness of your analysis and understanding of complex data patterns and relationships can be significantly impacted by your choice of graph database visualization tool. An optimal tool enables swift exploration of graph data connections, identification of trends and behavior patterns, and rapid decision-making based on well-informed insights.

Ready to get started with PuppyGraph? Download the forever free PuppyGraph Developer Edition or begin your free 30-day trial of the Enterprise Edition today.

Lei Huang, Chief Architect and co-founder of PuppyGraph, with over 10 years of experience in developing and managing high-performance data platforms. Lei was a Staff Software Engineer at Instacart, where he co-led the core payments team, led a major overhaul of the payments stack, integrating with various third-party systems. Prior to Instacart, Lei was the tech lead of Google payments full stack team, grow the team from 4 to 24 engineers.Lei is a three-time Google Code Jam world finalist, took 6th place in New York, and a two-time ACM/ICPC world-finalist with his team ranking 14th worldwide in Stockholm.

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