networkm 0.0.2

Dynamic network models in python.

networkm

Network Models based on networkx MultiDiGraph objects endowed with dynamics. Graph Functions contains various manipulation tools for graph objects. Model Functions contains methods of simulating these graphs as coupled, time-delayed differential equations with noise, with current support for Boolean functions / Boolean networks. Network Class contains the culmination of these functions in a single BooleanNetwork class object for pipelined analysis. PUF Functions analyzes these networks in the context of physically unclonable functions. Accelerated with numba.

Install

pip install networkm

How to use

import networkm
from networkm import *

This package models Boolean Networks with dynamics of the form

$\tau_{i}\frac{dx_{i}}{dt}=-x_{i}(t)+f_{i}[pred_{i}(t-delays)]+noise$

where tau is a time-constant, f is a logical function, $pred_{i}$ are the nodes flowing into node $x_{i}$ after some time-delay along each edge, and the noise term is random.

We can quickly simulate entire distributions of complex networks and analyze their statistics:

bne=BooleanNetworkEnsemble(classes=3,instances=3,challenges=3,repeats=3,
                           g = (nx.random_regular_graph,3,256),f=XOR,tau=(rng.normal,1,0.1),a=np.inf,
                           edge_replacements=dict(a=np.inf,tau=(rng.normal,0.5,0.05),
                                                  f=MPX,delay=(rng.random,0,0.5)),
                           delay=(rng.random,0,1),dt=0.01,T=25,noise=0.01,hold=(rng.normal,1,0.1),
                           decimation=None)
plot_mu(bne.data)
plot_lya(bne.data)

query : Elapsed time: 7.9315 seconds

bne[0].plot_graph()

Or start more simply, and consider a Ring Oscillator / Repressilator: https://en.wikipedia.org/wiki/Ring_oscillator , https://en.wikipedia.org/wiki/Repressilator. Real-world implications in e.g circuit design and gene-regulatory networks.

The system is a ring of 3 nodes which connect to their left neighbor and cyclically invert eachother.

g=ring(N=3,left=True,right=False,loop=False)
print_graph(g)

|Node|Predecessors|Successors|
|0   |1           |2         |
|1   |2           |0         |
|2   |0           |1         |

We model this with the simplest case, as follows. We give each node the NOT function. This function is executed differentially with a time-constant of 1. The node receives its neighbors state instantly; we put no explicit time-delays along edges, and include no noise. We initialize one node to 1, and hold all nodes at their steady-state value from this configuration for the default value of one time-constant. Then they are released and have the following dynamics:

ro=BooleanNetwork(g=ring(N=3,left=True,right=False,loop=False),
                 f=NOT,
                 tau=1,
                 delay=0,
                 noise=0, 
                 a=np.inf, #this makes the derivative integer-valued; see `sigmoid` function
                 init=[1,0,0],
                 hold=None,
                 edge_replacements=None,
                 T=15,
                 dt=0.01
                )
fig_params(reset=True)
ro.plot_timeseries()

The dynamics are not restricted to the edges of a hypercube, allowing Boolean Networks to explore regions of the analog phase space in ways that traditional models forcing only binary valued states don't capture:

ro.plot_3d()

For a more complex example we consider a "Hybrid Boolean Network" composed of a multiplexer - which forces inital conditions using a clock - connected to a chaotic ring network, which executes the XOR function. This has real-world implications in e.g cryptography and physically unclonable functions (PUF) as an HBN-PUF - see https://ieeexplore.ieee.org/document/9380284.

More explicitly, we consider a 16-node ring where each node executes the 3-input XOR function on itself and its two neighbors. We include noise, time-delays, different rise/fall times for tau, and replace each node with itself + a multiplexer that sets the initial condition and copies the state thereafter, with its own set of dynamical constants.

b=BooleanNetwork(g = nx.random_regular_graph(3,16),
          a = (rng.normal,15,5),
          tau = (rng.normal,[0.5,0.4],[0.1,0.05]),
          f = XOR,
          delay = (rng.random,0,1),
          edge_replacements = dict(
              delay = (rng.normal,0.5,0.1),
              a = (rng.normal,15,5),
              tau = (rng.normal,[0.2,0.15],[0.05,0.05]),
              f = MPX            
          ),
          T = 15,
          dt = 0.01,
          noise = 0.01,
          init = None,
          hold = None,
          plot=False,
         )

b.plot_graph()

We can quickly analyze differences between e.g randomly shuffled attributes and noise:

chal=b.random_init()
x,x0,y,y0=b.integrate(init=chal,noise=0.),\
          b.integrate(init=chal,noise=0.1),\
          b.query(instances=1,challenges=[chal],repeats=1,noise=0)[0,0,0],\
          b.integrate(noise=0.1) #parameters been shuffled by query
plot_comparison(x,x0,y,y0,i=0)

sidis.refresh()