https://github.com/benmaier/biasedrandomwalks

Provides classes around biased random walks on networks.
https://github.com/benmaier/biasedrandomwalks

biased-random-walk graphs networks random-walk

Last synced: 10 days ago
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Provides classes around biased random walks on networks.

• Host: GitHub
• URL: https://github.com/benmaier/biasedrandomwalks
• Owner: benmaier
• Created: 2018-07-02T23:24:56.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-07-03T21:35:56.000Z (over 6 years ago)
• Last Synced: 2024-10-11T11:08:03.497Z (2 months ago)
• Topics: biased-random-walk, graphs, networks, random-walk
• Language: Python
• Size: 31.3 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# BiasedRandomWalks

Provides classes around biased random walks on networks.

## Install 

    $ git clone [email protected]:benmaier/BiasedRandomWalks.git 

    $ pip install ./BiasedRandomWalks

## Examples

```python

import numpy as np

import matplotlib.pyplot as pl

import networkx as nx

from BiasedRandomWalks import BiasedRandomWalk

# Create a test Graph with weighted edges where weights are distances

G = nx.Graph()

N = 4

G.add_nodes_from(range(N))

G.add_edge(0,1,weight=1)

G.add_edge(0,2,weight=1)

G.add_edge(1,2,weight=1)

G.add_edge(1,3,weight=0.1)

G.add_edge(2,3,weight=0.2)

# define sink nodes

sink_nodes = [2,]

# define bias

gamma = 1

# initial distribution on transient

p0 = np.array([1,0,0])

# initial distribution on all

p0_all = np.array([1,0,0,0])

# initial base class (choose 'exponential' or 'scale free')

RW = BiasedRandomWalk(G, gamma, sink_nodes, bias_kind = 'exponential')

fig, ax = pl.subplots(2,2,figsize=(9,7))

# integrate up to this time step

tmax = 10

# ========== prob density on sinks =========

t, rho = RW.get_amount_of_walkers_arriving_at_sink_nodes(p0,tmax)

for i_s, s in enumerate(sink_nodes):

    ax[0,0].plot(t, rho[:,i_s], label='sink node '+ str(s))

ax[0,0].legend()

ax[0,0].set_xlabel('time')

ax[0,0].set_ylabel('amount of walkers arriving')

# ========== cdf on sinks =========

t, rho = RW.get_amount_of_walkers_arrived_at_sink_nodes(p0,tmax)

for i_s, s in enumerate(sink_nodes):

    ax[0,1].plot(t, rho[:,i_s], label='sink node '+ str(s))

ax[0,1].legend()

ax[0,1].set_xlabel('time')

ax[0,1].set_ylabel('total amount of walkers arrived')

# ========== prob density on all =========

t, rho = RW.get_amount_of_walkers_on_nodes(p0_all,tmax)

for s in G.nodes():

    ax[1,0].plot(t, rho[:,s], label='node '+ str(s))

ax[1,0].legend()

ax[1,0].set_xlabel('time')

ax[1,0].set_ylabel('amount of walkers on each node')

pl.show()

```