A wide variety of the data generated and collected every day comprises not only detailed information about the particular entities, but also the relations between them. Data of this kind are often referred to as ``networks'' and encompasses social, biological, financial and traffic data, for example. Typically, graphs are used to model these networks.

The analysis of graphs, or networks, covers a wide range of tools to extract new and valuable insights on various levels. For example these tools can be used to:

  • evaluate the robustness of networks against failures/attacks
  • find groups of similar as well as clusters of densely connected actors
  • quantify the influence/importance of entities on resouce flow/control 
  • explain and predict changes in the graph structure over time
  • visually explore the data to find interesting patterns and (inter)relationships

However, not least due to the fact that today, we have to deal with big and complex network data a single analysis is unlikely to capture the whole picture. Additionally, specific analysis techniques might also require different preprocessing steps of the very same dataset. Therefore, generally speaking, a network analysis workflow will entail multiple steps, i.e.,:

  1. data import
  2. data integration
  3. data transformation
  4. graph analysis
  5. graph visualization

The overall goal of BIGGR is to provide a new open-source software system that on the one hand features an efficient, powerful and extendable back-end, which offers miscellaneous tools for the analysis of massive graphs and, on the other, ships with a user-friendly and modular front-end to create workflows, concealing any complexity from the user.

In this respect, BIGGR contrasts favorably with classical databases, which lack flexibility, are comparably technical, and do not come with an elaborate support for network analysis algorithms and workflow creation.

As part of the IKT 2020 - Forschung für Innovation: Förderkennzeichen 01IS16030 by the Bundesministerium für Bildung und Forschung, BIGGR pools expertise from industry (KNIME GmbH) and academia (Universität Leipzig, Abteilung Datenbanken). While the KNIME Analytics Platform provides BIGGR with a graphical user interface to create, interact, modify, and trigger workflows in a evidentially user-friendly way, Gradoop ensures its efficient, reliant, and scalable execution.


BIGGR encompasses more than 30 nodes including a powerful graph-viewer to explore the graph structure. Despite of the limited number of available modules, our system has not only been proven useful for our industrial partners, but has also been well received by the audience at various occasions. 

BIGGR Workflow Example


Installation and Usage

The BIGGR update site for the KNIME Analytics Platform is:

To add an update site to your KNIME Analytics Platform:

  • Navigate to "File" → "Preferences" → "Install/Update" → "Available Software Sites"

  • Click "Add…​"

  • And either add a new update site by providing a URL via the "Location" field

  • Or, by providing a file path to a zip file that contains a local update site, via "Archive…​"

  • Finally, give the update site some meaningful name and click "OK"

For further details on how to install extension see this guide.

For further details about the Gradoop operators and their functionality see the Gradoop Wiki page.