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Fast filtering and animation of large dynamic networks


This project is a collection of two tools that filter and visualize large dynamic networks. More specifically, the tools perform the following functions:

  • The filtering tool. From a chronological sequence of weighthed graph links produce differential updates to a subgraph of the network delegated for visualization in a format of JSON events.
  • The visualizing tool. Produce movies of evolving graphs from a feed of the JSON events.

In addition, the filtering tool can send the network updates directly to Gephi Streaming API, which visualizes them interactively.

The filtering tool can be understood as a sliding time-window method with an exponential decay that is limited in computational complexity and memory usage. The method is introduced, described, and analyzed in the publication that is available at, using datasets released within this project. In case you decide to use our method or datasets we kindly request that you cite the publication.


The tools have been tested under Linux and Mac OS systems.

The filtering tool is written in C++ and needs to be compiled using gcc version 4.x. The visualizing tool is a Python script requiring Python 2.6 or higher, located under the /scripts/ subdirectory.

Before proceeding to next point of these instructions please check that all the required dependencies specified below are present on your system.

The filtering tool has the following dependencies on the external libraries and command line tools:

The visualizing tool has the following dependencies on the external libraries and command line tools:

Configuring and building the filtering tool

While the visualizing tool is written in Python and can be used with little configuration, the filtering tool is written in C++ and must be compiled from source using gcc 4.x (tested using 4.7.2).

When installing dependencies, try to install as many as possible using a package manager like apt. Later version of Boost introduce a bug that causes the tool to fail, so it must be version 1.44 to 1.55. jsoncpp can be built using SCONS. cpp-netlib requires cmake and clang. It is possible that Boost's network libraries won't be installed, but these libraries can be found bundled with cpp-netlib. It may also be necessary to add a symbolic link to the boost binaries under the src/ directory.

To configure the system before the compilation of the filtering tool one needs to enter paths to the corresponding installed libraries in the configuration file present in the parent directory of the project. After inputting the paths to the configuration file, in order to compile the code run:


This command will compile into the executable visualize_tweets_finitefile and copy the executable to the parent directory of the project. Note that before running visualize_tweets_finitefile one needs to tell the linker where the compiled libraries are. The simplest way of achiving it is by following the instructions that are printed after is successfully finished. The other possibility is to use for launching the tools. This small script configures the paths itself.

Configuring and running the visualization tool

In order to create videos using these tools, an edge list must be input into the filtering tool. The filtering tool will create a json file which is then input into the visualization tool. The visualization tool will create a set of .png images and then combine them into a .avi video file using the mencoder command line tool. If mencoder is not installed on the system, the visualization tool will fail silently and simply not output a video.

Among other settings, the color scheme of the resulting movie can be changed in the settings file scripts/

Running Tests

Both the tools can be tested by running:

./ test

The filtering tool can be tested by running from the parent directory:

./visualize_tweets_finitefile --input data/test.sdnet --output data/test

The visualizing tool does not require installation and can be launched from the parent directory of the project:

python scripts/ data/test.json

Re-creating the demo movies

The tools are released together with four datasets that reside in the directory data. For each of the datasets a demo movie has been created using the tools:

A detailed description of the demo movies is available the full publication (available at

The script has been created to automatize the recreation of the demo movie and to store the values of the parameters used for their generation.

To launch the filtering tool in order to convert the demo sdnet files that are located in the directory data to json files:

./ demo-diffnets

To launch the visualizing tool in order to create movies from the jsonfiles stored in the directory data and save them as avi in the directory movies:

./ demo-movies

Input format

The dynamic network that is given as the input to the filtering tool can have multiple edges and it can be either weigthed or unweigthed. The input file has to be sorted in chronological order with the epoch time used as time stamps. The input files have the following format for each of its lines:

t1 n1 n2 w1
t2 n1 n3 n4 w2

Where t1 is an epoch time, n1 stands for node 1, n2 stands for node 2, and w1 is the corresponding weight of the connection(s).

  • Weigthed links - files with the extension wdnet, to run the filtering method for this format use the --weigthed flag
  • Unweigthed links - files with the extension sdnet, the same format, except the weights are not stored in the files

One can see examples of wdnet and sdnet input files in the directory data.

Creating your own movies

You can use the tools to create your own movies of dynamic networks. To learn how to set the parameters of the tools please see Appendix B of our publication (available at The parameters of the filtering tool are to be provided as arguments to visualize_tweets_finitefile (run visualize_tweets_finitefile -h for details), while the parameters of the visualizing tool are stored in the configuration file scripts/

The length of the resulting video can be controlled by using the time_contraction argument sent to the filtering tool. The time contraction is the ratio of the length of time (in seconds) that the input edge list covers to the length of the video (in seconds). For example, if the time span of the edge list is 1 week, and you wanted a 30 second video, the time contraction parameter would be 20160 (1week * 7days * 24hours * 60minutes * 60seconds / 30seconds = 604800 seconds / 30 seconds = 20160)

Launching interactive visualizations

Before launching interactive visualizations one needs to run server in the Graph Streaming API in Gephi. To do this launch Gephi, start new project and in the panel called Streaming select Server, and run it. To make the visualization look better in the Labels panels turn on node labels, select option Size proportional to Node size, and with the slider reduce the size of labels by half. Finally, get a graph layout running (e.g., Fruchterman) in the panel called Layout.

To launch stream the visualization directly to Gephi for the selected json file:

./ gephi json_file server_ip_address time_contraction

The parameter time_contraction is important here. If is low (e.g., 100) for a dataset that has a time span of 100 years, then the visualization will last 1 year, needless to say way to long. To learn how to set the parameters of the tools please see Appendix B of our publication (available at


Fast filtering and animation of large dynamic networks







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