ManyNets: Visualize Many Networks Simultaneously


Project Description

ManyNets is a network visualization tool with tabular interface designed to visualize up to several thousand network overviews at once. This allows networks to be compared, and large networks to be explored using a divide-and-conquer approach. For example, comparing different social networks can provide insights into the underlying causes for their differences; or a social network with temporal data may be subdivided into temporal slices, which can then be examined to locate temporal patterns or regions and periods change. Network subdivision is also possible based on motifs (small patterns of connectivity), clusters, or network-specific attributes.


A collection of networks is presented in a table, where each row represents a single network. Columns represent statistics, such as link count, degree distribution, or clustering coefficients. Details are available on-demand, either in the form of larger views or as SocialAction views of specific networks.

The use of a table allows easy comparisons between rows (networks) and columns (their statistics). Computationally expensive statistics (e.g.: network diameter) are only added if the user explicitly requests them. Users can also add custom columns by entering expressions in Python. A similar interface can be used to specify expressions for filters, selections, and sorting column definitions.

Column summaries, placed on top of the column headers, provide abstracts of the contents of their columns. The summaries also support direct user interaction, and reflect application state by highlighting values that correspond to currently-selected rows. The histograms used in these summaries provide quick assessment of the distributions of values within a set of networks.

Video Demonstrations

Title Screen 



(click to view in 1024x768)

FilmTrust scenario:

Overview of the FilmTrust network, and description of a typical analysis using ManyNets.


Demo Summary:

Demonstrates main features of ManyNets; used as tutorial for an experiment to study the usability of the tool.

4 minutes 58 seconds








  • Manuel Freire - Fulbright-Postdoc Researcher from Spain.

  • Catherine Plaisant - Research Scientist, UMIACS, Associate Director of Research at HCIL.

  • Ben Shneiderman - Professor, Computer Science, Researcher (and Founding Director) at HCIL.


Our first accepted publication is a two page write-up for the VAST'09 Social Network and Geospatial challenge (see the  task description).

  • Freire-Morán, Manuel. Finding and Ranking Patterns in the VAST 2009 Social Network Challenge, VAST 2009 compendium, Aug 2009 pdf.

We will submit papers to CHI 2010 and other venues as the project matures.


User manual


Sponsors and Partners

This project is partially supported by Lockheed Martin. Manuel Freire is supported by a MEC/Fulbright grant.

Other Related Projects from HCIL

SocialAction: a social network analysis tool that integrates visualization and statistics to improve the analytical process.

SocialAction: a social network analysis tool that integrates visualization and statistics to improve the analytical process.


Web Accessibility