The Best Network Visualization Tools in 2025
Posted Wednesday, April 23, 2025
Network visualization software can uncover relationships and patterns within data. Although it is commonly used in social network and organizational analysis, its applications extend much further. It can be instrumental in text analysis, generating knowledge graphs, analyzing travel patterns, studying event co-occurrences, and examining various other types of activities beyond the social domain. Some of the insights can also be used for feature extraction to improve machine learning models. Below we present a comparison of the best tools and software libraries available today (as of 2025) both for beginners and tech-savvy users.
Most of the network visualization and analysis tools available today fall into one of the following categories:
- Online applications and analytics platforms (e.g. InfraNodus, Graph Commons, Rhumbl)
- Desktop applications (e.g. Gephi, Pajek, NodeXL)
- Python and R-based software libraries and packages (e.g. NetworkX, iGraph)
- Javascript libraries (e.g. Cytoscape, Graphology, Sigma.Js)
- Graph databases (e.g. Neo4J, TigerGraph)
- Excel plugins (e.g. NodeXL)
If you know how to program and need a high degree of customization, you can go for for the Python or R-based software packages. These can be used to build your own applications and also provide maximum control over your data. For more advanced users, installing a graph database, such as Neo4J, also gives you the access to the analytical tools included with the package.
If you are a data scientist, you might want to use a desktop application or an online platform. These can save the development time and provide direct access to graph analytics and various network metrics.
Note: to make the comparison more objective, we will use the Diseasome dataset that contains correlations between diseases based on shared gene mutations. In this dataset, the diseases are connected if they are both related to at least one common gene mutation.
Online Network Visualization Software
If you would like to build a graph using your browser and easily share it with the world, you can use InfraNodus, Rhumbl, or Graph Commons.
Rhumbl and GraphCommons are a bit similar in a way that they have an interface that enables you to customize the look of your graph. However, they provide very rudimentary network analysis features. This is where InfraNodus truly stands out, as it provides not only a graph constructor, but also advanced network analytics that are lacking in the other tools.
All of these tools either offer free accounts or trials, so you can try them online. Paid accounts are relatively accessible, starting at €9 a month, and some of these tools also offer an API. All the tools let you share the data using embeddable iFrames, URLs, or plain images. Only InfraNodus offers high-resolution vector-based export, which is suitable for print publications.
In terms of the usability, it really depends on your preferences, we describe the peculiarities of every tool below:
1. InfraNodus: A Universal Tool for Network Visualization
InfraNodus is a network visualization and analysis tool. You can import your data from a spreadsheet or a Gexf file, edit the graph manually, or use its live text-to-graph editor to quickly create your graph. InfraNodus will automatically provide graph analytics, such as community detection and node influence measures normally available in advanced tools such as Gephi or NetworkX.
The graph in InfraNodus can be used as an heuristic device to explore network analysis insights obtained from the data. For example, you can select a node or a community and remove it from the graph to see how community structure and influence distribution changes. This is very useful for understanding the importance of specific clusters or exploring the context around them.
Below is an example of a graph visualizing the Diseasome dataset created with InfraNodus. We can see the main clusters of diseases (with AI-generated names for the clusters) as well as the most influential nodes in the disease network:
In addition to providing community detection and centrality measures, InfraNodus also calculates network structure diversity based on modularity measures and distribution of influence across community clusters. This can be used to estimate resilience of a network or to optimize it for specific objectives.
InfraNodus also offers AI-based recommendation system that traverses the graph and recommends new connections that bridge the structural gaps in your data. Every piece of data you obtain using InfraNodus can be exported as a spreadsheet or JSON for further analysis. The graphs can also be exportd in a high-resolution vector format (SVG), which is suitable for large-format print.
Pros:
• Easy to use (analysis presets)
• No need to code
• Advanced analytics (network structure, structural gaps)
• Export options
• Built-in AI
• Offers an API with graph insights
Cons:
• Requires subscription
• Not as customizeable as other tools
• Not suitable for graphs with more than 500 nodes
2. Graph Commons: Graph Storytelling Tool
With Graph Commons, it can take a while to build a graph, because you have to specify a name and category for every node that you want to add. The advantage is that you can provide lots of meta data and additional detail about each node and edge, which may be useful for some applications.
The most typical use case for Graph Commons is to create a presentation that would allow the audience to get a general overview of the network and to learn more about the nature of relations between them.
The problem, however, is in adding the data to GraphCommons. You can import a spreadsheet or CSV, but you have to save it in a very specific format as a spreadsheet, compatible with Graph Commons template, so it may be difficult to convert your existing data into their format. I tried to do that for the Diseasome dataset, and it was impossible due to the errors on the side of GraphCommons. Their support doesn't seem to respond on Discord (multiple unanswered messages). So I used their sample graph instead:
As you can see, GraphCommons can build large graphs and provide some analytics, but its scope is very rudimentary. You can see the main clusters and the most influential nodes, but it's not possible to get more details about each cluster / node, retrieve the centrality measure of the nodes it's connected to, or to remove them and recalculate analytics in an interactive way. It is also not clear what metrics they use, so GraphCommons may be suitable for presentational use but will probably not be a good choice for research as there's no peer reviewed papers that explain its algorithms.
Pros:
• Good for storytelling and sharing
• Provides a good general overview
Cons:
• Does not have detailed network analytics
• Only supports their own proprietary formats
• Lack of customer support (is the tool abandoned?)
• Lacks customization options
• No AI functionality
Gephi Lite: Legendary Graph Visualization Platform
Gephi Lite is a web version of the popular graph visualization and analysis tool. It is a great option for those who want to get started with graph analysis without the need to install anything on their computer.
Gephi Lite can import most common graph file formats and you can also create the graph manually (although this will take time because it does not have text-to-graph capability). You can then apply various graph layout and community detection and ranking measures based on network science metrics to get a structured view of the network.
Below is the visualization of the same Diseasome network we use in the previous demonstration. You can clearly see the clusters and the most important nodes. However, as Gephi is much more technical than other platforms, it may be more difficult to interpret and share your results.
In order to create the graph, you'll need to play around with some options and you need to know how graph metrics works, so it's suitable for advanced users only. However, once you get into it, you can get some really interesting insights.
Pros:
• Built by a trusted team who has worked on this tool since 2009
• Best customization options
• Works for large graphs
• Free and open source
Cons:
• Has a steep learning curve
• You need to generate network files yourself
• Very technical, so limited to advanced users
• No AI functionality
Desktop Applications: Gephi, NodeXL, and Pajek
If you don't mind installing a desktop tool and spending some time to learn how it works, you might be intersted in Gephi, NodeXL, or Pajek. These are advanced graph analysis platforms that provide most of the metrics normally used in network science. Here are some interesting facts about each of them:
Gephi: Great for Analysis and High-End Visualization
Gephi is a wonderful tool that has both network metrics and advanced visualization customization. You can calculate nearly every measure that you can think of and align the nodes in many different ways. You can also design the look for your graph once it's exported as an image: e.g. choosing the shape of the edges and of the nodes, defining the colors for each community, etc. Gephi is free and is cross-platform as it runs of Java. You can even run it in server mode, so it can be integrated into your existing applications using its API.
NodeXL: Best Suited for Social Network Analysis
NodeXL will only run on Windows and its interface is a bit old-school. However, it can still be a good choice for performing social network analysis. NodeXL seems to be not so frequently updated these days and they offer a confusing range of options: for example, their browser-based version is nowhere to be found, while the Pro desktop version is a bit cumbersome and slow.
Pajek: The Old Classics
Pajek is perhaps one of the first popular network analysis tools available on the market. It only runs on Windows and was last updated in 2009, so we keep it here for historical purposes only.
Software Packages: NetworkX, iGraph, Graphology
If you are a tech-savvy who likes programming, you might be interested in Python- or R-based NetworkX and iGraph or Javascript-based Cytoscape and Graphology. You would obviously need to program your use case manually, but all those libraries have multiple examples and templates. All of these packages are free and open-source.
NetworkX: The Popular Python Classics
NetworkX is the most popular Python network analysis package with some visualization capabilities. It is frequently used in various types of applications and is the industry standard. It is free and open-source, you also benefit from active developer community and multiple examples that can help you build towards your use case.
iGraph: Faster Processing
iGraph is perhaps better suited for building large graphs as its base code is implemented in C, so it may be faster than NetworkX if you are concerned about the speed or have to work with the large graphs. Here is an interesting benchmark comparison of the both tools where iGraph is definitely a winner. Some people complain that iGraph is harder to use and has less documentation than NetworkX though.
Cytoscape: Javascript Network Analysis and Visualization Powerhouse
Cytoscape is a javascript-based library that was initially built for analyzing biological networks. So it can handle vast amounts of data, has multiple network metrics, and boasts its own visualization package. Some of its metrics is used for graph analysis in InfraNodus.
Graphology: A Light Graph Analysis Library
The advantage of Typescript-based Graphology is that it's the newest package from the list, so it's really lightweight and less bloated than the others. It also has a tight integration with the amazing Sigma.Js network visualization package, making those two a true gem when it comes to visualizing and analyzing both small and large networks.
Graph Databases: Neo4J, Titan, OrientDB, Tigergraph
If you are deeply immersed into network analysis, you might want to install your own nosql graph database. All of the graph databases available today offer their own graph analysis algorithms, which can be ran on vast amounts of data. You would need understand their query language and know how to program, but for large-scale applications these will provide the highest level of customization.
Neo4J: Widely Used and Reliable
Neo4J is one of the oldest and most popular graph databases. It offers advanced graph analytics and easy-to-use Cypher graph query language. They have a community version that is free and also offer their own hosted instances, which is great if you want to set up a test DB to play with. The default visual interface is a bit laggy, but if you're happy to code, you won't be disappointed.
TigerGraph: Built-In Machine-Learning
TigerGraph is the most hyped graph database today as they offer built-in machine learning capabilities. They also claim to be the fastest graph database on the market.
Try It Yourself
You can perform network analysis and visualization on your own data using InfraNodus:
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