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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 an individual social network can also be subdivided into temporal slices, which can then be examined to locate temporal patterns or regions and periods change. Networks can also be subdivided and compared 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.

Selecting columns

Sorting and ranking with a custom query

Details of a distribution. The green portion corresponds to selected rows.

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




Short demo Short demonstration of ManyNets (under 6 minutes) mp4
Training video Training video: ManyNets used to analyze FilmTrust (under 9 minutes) mp4



  • 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.


We presented ManyNets at the CHI 2010 conference. Previously, we used a development version to submit an entry to the VAST 2009 Social Network and Geospatial challenge (see the task description). A paper on overview generation in the latest version of ManyNets is currently under review.

    • Awalin Sopan, PJ Rey, Jae-wook Ahn, Catherine Plaisant, Ben Shneiderman. The Dynamics of Web-Based Community Safety Groups: Lessons Learned from the Nation of Neighbors, February 2012 [pdf].
    • Awalin Sopan, Manuel Freire-Morán, Catherine Plaisant, Jennifer Golbeck, Ben ShneidermanDistribution Column Overviews in Tabular Visualizations, April 2010 [pdf].
    • Manuel Freire-Morán, Catherine Plaisant, Ben Shneiderman, Jennifer Golbeck. ManyNets: An Interface for Multiple Network Analysis and Visualization, August 2009 (revised CHI 2010 version available at the ACM Digital Libary) [pdf].

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


    October 8 HCIL presentation (for the HCIL brown-bag presentation series) - DOWNLOAD Source code - 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.

    Graph visualization projects at HCIL: A list of graph visualization projects in the lab.

    Hierarchical Clustering Explorer: Exploratory analysis for multidimensional data sets.