Software

Network Inference

Check out the implementations of NetInf, NetRate, MultiTree and InfoPath. The four of them are network inference algorithms from diffusion traces. NetInf and MultiTree exploit submodularity and NetRate and InfoPath exploits convexity to make the inference problem tractable. NetInf, MultiTree, and NetRate support static networks, while InfoPath supports both static and dynamic networks

• NetRate (Matlab/CVX)

• InfoPath (C++/Snap)

• NetInf (C++/Snap)

• MultiTree (C++/Snap)

You can find more information about the algorithms in Publications. Please, feel free to send any suggestions, comments, bugs or alternative implementation (Python, etc.) of NetInf, NetRate and/or MultiTree to manuelgr[at]tuebingen.mpg.de.

Influence maximization

Check out the implementation of InfluMax. It is a influence maximization algorithm for continuous time diffusion networks. InfluMax exploits submodularity and is capable of evaluating influence using CTMCs.

• InfluMax (C++/Snap)

You can find more information about the algorithm in Publications. Please, feel free to send any suggestions, comments, bugs or alternative implementation (Python, etc.) of InfluMax to manuelgr[at]tuebingen.mpg.de.

Influence estimation

Check out the implementation of ConTinEst. It is a highly efficient influence estimation algorithm for continuous time diffusion networks. ConTinEst uses randomization to scale influence estimation to networks with million of nodes and it can be used as a building block of InfluMax.

• ConTinEst (C++)

You can find more information about the algorithm in Publications. Please, feel free to send any suggestions, comments, bugs or alternative implementation (Python, etc.) of ConTinEst to dunan[at]gatech.edu.