README.md

pyconnie

pyconnie is an implementation of Seth A. Myers and Jure Leskovec's approach to inferring latent social networks by exploiting the convexity of the problem.

You can find the paper here: http://arxiv.org/abs/1010.5504

Installation

This package requires numpy==1.8.1 and scipy==0.14.0. You should have those installed before you get started.

You can install pyconnie using setup.py like

python setup.py install

Or from the Python Package Index like

pip install pyconnie

You can run the tests with

python setup.py test

Example

The example below solves for the 0th column of the adjacency matrix, A with high-precision and low recall.

from scipy.optimize import fmin_tnc 
import numpy

from pyconnie.diffusion import Diffusion, Diffusions
from pyconnie.connie import convex_formulation

A_true = numpy.random.rand(4, 4)
for i in xrange(4):
    for j in xrange(4):
        if i == j:
            A_true[i][j] = 0
        else:
            if A_true[i][j] > 0.5:
                A_true[i][j] = 0.0
            else:
                A_true[i][j] *= 0.50

D = Diffusions(diffusions=[
        Diffusion(A_true) for d in xrange(1000)]
)

A_guess = numpy.array(numpy.random.rand(1, 4))

bounds = [(0, 1) for x in A_guess[0]]
bounds[0] = (0,0)

print fmin_tnc(func=convex_formulation,
               x0=numpy.array(A_guess),
               args=(0, D),
               bounds=bounds,
               approx_grad=True)