Alternative to Gephi Network Analysis Tool

Gephi is a powerful network visualization software that is used for creating and analyzing networks. However, it may be complex to set up and use, offers limited import options, and demands significant attention to adjusting the settings before getting concrete insights.

InfraNodus can be used as an alternative to Gephi. It's a true plug-and-play platform: you can upload any existing graph or import external data from multiple sources and start getting insights in seconds based on state-of-the-art network analysis algorithms.

The network graphs generated by InfraNodus are fully interactive, so you can use them to explore the network, remove nodes to see how it affects the network, and share your findings online or via high-resolution SVG vector images suitable for publications.

Take a look at the features in the table below that we offer right out of the box to help you get started easily.

Looking for a Gephi alternative? Try InfraNodus!

Gephi vs InfraNodus

In order to compare the two products, we will use the Diseasome dataset that contains correlations between diseases based on shared gene mutations. We import the Gexf file into both tools and see which insights we can get and how we can then interact with the network graph to explore underlying data and relationships.

Gephi: Great for an Overview

Gephi has a web-based version (Gephi Lite) that has most of the functionality of the desktop version. In order to make it work, we have to make some adjustments and ask the software to apply community detection algorithm, color the nodes by the clusters identified, and then rank the nodes by betweenness centrality and ask Gephi to rank their size by degree. This takes some time, but as a result, we get a graph that shows us the clusters of diseases ranked by their local importance.

 

Since Gephi provides numerous adjustable parameters, familiarity with interpreting network graphs allows you to gain valuable insights into both the overall network structure and individual nodes and relationships. InfraNodus, by contrast, directly generates insights from the graph's structure.

 

InfraNodus: Get Insights from the Graph

Now we visualize the same Gexf file in InfraNodus. By default, InfraNodus applies community detection algorithm, colors the nodes by the clusters identified, and ranks the nodes by betweenness centrality. This helps us see which diseases may be correlated with others.

 

As you can see, the analytical insights in InfraNodus are much more detailed. We can quickly in a table format which nodes belong to the same cluster and a list of the most influential ones.

The time and cognitive resources that you save setting up the initial visualization in InfraNodus can now be used to explore the specifics of the graph. None of the functionalities below are available in Gephi out-of-the-box.

 

Identifying Structural Gaps

For instance, you can switch to the Content Gaps tab in the Analytics panel and identify the structural gaps. These gaps have high potential for generating new ideas and insights as they usually indicate a very specific intricate aspect of a network. In our case, we can see that tumors do not co-occur with endocrine disorders, which may give us important insights about the body as well as for future research in treatments and gene therapy.

 

Finding Effective Inter-Community Brokers

The same analytics tab has the "Latent Nodes" list, which shows the nodes with a high ratio of influence to frequency (betweenness centrality to degree). These are important elements that could be missed in initial analysis, but they play an important role being brokers between different clusters. In our example, those are the diseases that we need to watch especially well because they act as very efficient bridges between different community clusters: "gastric cancer" and "placental abruption" among them. The presence of these diseases might indicate the potential for multiple other disorders to emerge (even from a completely different category).

 

Node Removal, Recalculuation, and Graph Traversal

InfraNodus can also be used to quickly recalculate all the metrics once a certain node is removed from the graph. This helps uncover underlying clusters and nodes that would be normally visible from a general overview.

 

Using AI to Get Insights from the Graph

Built-in AI can be used to query this knowledge graph using the underlying GraphRAG system to explore each cluster in mode detail and generate interesting insights.

For instance, we can select two diseases and use the trove of human knowledge (contained in an LLM) to learn more about the possible links between them:

 


 

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