https://github.com/networks-learning/infopath

Infopath C++/SNAP implementation
https://github.com/networks-learning/infopath

Last synced: about 6 hours ago
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Infopath C++/SNAP implementation

• Host: GitHub
• URL: https://github.com/networks-learning/infopath
• Owner: Networks-Learning
• Created: 2012-12-25T22:26:32.000Z (almost 12 years ago)
• Default Branch: master
• Last Pushed: 2012-12-25T22:51:15.000Z (almost 12 years ago)
• Last Synced: 2024-04-16T02:14:55.481Z (7 months ago)
• Language: C++
• Size: 738 KB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: ReadMe.txt

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README

        
========================================================================

    Structure and Dynamics of Information Pathways in On-line Media    

========================================================================

Diffusion of information, spread of rumors and infectious diseases are all

instances of stochastic processes that occur over the edges of an underlying

network. Many times networks over which contagions spread are unobserved and 

need to be inferred from the diffusion data. Moreover, such networks are often 

dynamic and change over time.

We have developed an on-line algorithm that relies on stochastic gradient

descent to efficiently infer dynamic networks based on information diffusion 

data. We assume there is an unobserved dynamic network that changes over time,

while we observe the results of a dynamic process spreading over the edges

of the network. The task then is to infer the edges and the dynamics of the

underlying network.

For more information about the procedure see:

  Structure and Dynamics of Information Pathways in On-line Media

  Manuel Gomez-Rodriguez, Jure Leskovec and Bernhard Schölkopf

  http://www.stanford.edu/~manuelgr/dynamic/

  

In order to compile on MacOS: 'make' OR 'make opt'.

In order to compile in Linux: 'make linux' OR 'make opt_linux'. 

The code should also work in Windows but you will need to edit the Makefile.

'make opt' and 'make opt_linux' compile the optimized (fast) version of the code.

Usage:

Infer the network given a text file with cascades (nodes and timestamps):

./infer -i:cascades.txt

All arguments are shown any time ./infer is run.

/////////////////////////////////////////////////////////////////////////////

Format input cascades:

The cascades input file should have two blocks separated by a blank line. 

- A first block with a line per node. The format of every line is ,

- A second block with a line per cascade. The format of every line is ;,,,,,...

/////////////////////////////////////////////////////////////////////////////

Additional Tool:

In addition, generate_nets is also provided. It allows to build time-varying Kronecker and Forest-Fire networks and generate cascades

with exponential, powerlaw and rayleigh transmission models. Please, run without any argument to see how to use them.