Touch Graph
touch graph navigation

We are a team of interface designers who share a passion for creating better ways to visualize and interact with information.

We believe existing data sets can reveal new insights when visualized effectively.

Our focus is on creating tools that enable decision makers to display, navigate, and analyze complex data simply and intuitively.

Why visualization?

Individuals and organizations have more vital information at their fingertips than ever before. Traditional search engines provide a way to sift through this data. However, the greatest insights can be achieved not by sifting, but by looking at the big picture to see how items are connected.

Visualization goes beyond lists to reveal larger-scale patterns. Results are displayed in context – one can see how they fit into the big picture, how they relate to each other, and how they connect to metadata such as subjects and authors.
Graph of social media and sniki wiki

Why TouchGraph?

TouchGraph's approach is unique because our highest priorities are utility and ease of use. We start with the premise of a scientific or business application, and work to create a clear and intuitive interface that gets our customers the results they need. We avoid superfluous artistic effects in favor of a clear presentation.

Our applications are designed to simplify navigation, filtering, and visual metaphors. They reveal important relationships and details in your data without reducing the richness of the information itself.


you have years of relational information at your fingertips now you can learn from it visualize your data in an instant by importing an excel spreadsheet

* 100% java technology.
* A range of different relationship types are supported. Edges can be directed, undirected, and can show flow in both directions.
* Text and numerical attributes can be associated with nodes and edges. Tables display the attributes and allow sorting.
* Images can be associated with nodes.
* Advanced cluster computation reveals inherent groupings.
* Co-citations and co-occurrence analysis clarifies dense networks.


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