Barabasi-Albert模型，错误指数

问题描述

我正在尝试使用Barabasi-Albert模型生成无标度网络.该模型预测度分布遵循p(k)〜k ^ -3，但我的显示为k ^ -2.

I'm trying to generate a scale-free network using the Barabasi-Albert model. The model predicts a degree distribution that follows p(k) ~ k^-3 but mine shows k^-2.

该算法取自Barabasi的书，网址为: http://barabasi.com/networksciencebook ，这是相关的段落:

The algorithm was taken from Barabasi's book at this URL: http://barabasi.com/networksciencebook,here is the relevant paragraph:

Barabasi的算法

这是我的代码，有人可以帮我找出问题所在吗?

Here is my code, could someone please help me figure out what is wrong?

import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
plt.rcParams["figure.figsize"] = (15,6)

#initialize values
N = 10000
k = 2
m = int(k / 2)

#initialize matrices
adjacency = np.zeros((N,N))
degrees = np.zeros(N)

#add links
for i in range(N):
    degrees[i] = m
    for c in range(m):
        # choose a node with probability proportional to it's degree
        j = np.random.choice(N, p = degrees / (2 * m * i + m + c))
        degrees[j] += 1
        adjacency[i][j] += 1
        adjacency[j][i] += 1


def get_binned_data(labels, values, num):
    min_label, max_label = min(labels), max(labels)
    base = (max_label / min_label) ** (1 / num)
    bins = [base**i for i in range(int(np.log(max_label) / np.log(base)) + 1)]
    binned_values, binned_labels = [], []
    counter = 0
    for b in bins:
        bin_size = 0
        bin_sum = 0
        while counter < len(labels) and labels[counter] <= b:
            bin_size += values[counter]
            bin_sum += values[counter] * labels[counter]
            counter += 1
        if(bin_size):
            binned_values.append(bin_size)
            binned_labels.append(bin_sum / bin_size)
    return binned_labels, binned_values


labels, values = zip(*sorted(Counter(degrees).items(), key = lambda pair:
pair[0]))

binned_labels, binned_values = get_binned_data(labels, values, 15)

fig, (ax1, ax2) = plt.subplots(ncols = 2, nrows = 1)
fig.suptitle('Barabasi-Albert Model',fontsize = 25)

ax1.loglog(binned_labels, binned_values, basex = 10, basey = 10, linestyle =
'None', marker = 'o',  color = 'red')
ax1.set(xlabel = 'degree', ylabel = '# of nodes')
ax1.set_title('log-log scale (log-binned)',{'fontsize':'15'})

ax2.plot(labels, values, 'ro')
ax2.set(xlabel = 'degree', ylabel = '# of nodes')
ax2.set_title('linear scale',{'fontsize':'15'})

plt.show()

推荐答案

您的代码未运行(np.random.choice中的概率不等于1).为什么不p = degrees/np.sum(degrees)?

Your code does not run (probabilities in np.random.choice do not sum to 1). Why not p = degrees/np.sum(degrees)?

根据维基百科，您需要从一些已经连接的节点开始，而您从零开始.另外，您可能应该在内部循环之后放置degrees[i] = m，以避免形成从节点i到其自身的链接.

According to Wikipedia, you need to start with some already connected nodes, whereas you start from nothing. Also, you should probably put degrees[i] = m after the inner loop to avoid forming links from node i to itself.

这可能会有所帮助，但我不清楚您如何生成学位图，因此我无法对其进行验证.

This might help, but it's not clear to me how you generate your degree plot, so I can't verify it.

这篇关于Barabasi-Albert模型，错误指数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！