这篇复杂网络论文，心累的要死！

需要这篇论文的复现。
论文名称：Dynamical Interplay between Awareness and Epidemic Spreading in Multiplex Networks
论文内容：在多路网络中构建了uau-sis模型，运用mmca计算出传播阈值，mc模拟计算出传播阈值
论文doi：https://doi.org/10.1103/PhysRevLett.111.128701
sic-hub网站上的论文：https://sci-hub.wf/10.1103/physrevlett.111.128701
要求：必须python或matlab，可以是这篇论文或在这篇论文的基础上改进的论文

网上找找资源，实现上比较复杂。

考虑时变遗忘因素的双层网络传播模型建立方法
可以参考下
https://www.xjishu.com/zhuanli/05/202111532649.html

这个不简单呐，最好能联系上作者

git找找有没有类似的资源，从零实现，比较难

给作者发个email，夸赞下他的文章写的很好，很仰慕他，能不能把实验的代码发给你学习下。一般都会给你的。不然不是你这个专业的，看懂这个论文很难的。

import numpy as np
import networkx as nx

def uau_sis_simulation(network, p, q, initial_infected, timesteps):
    num_nodes = network.number_of_nodes()
    states = np.zeros(num_nodes, dtype=int)
    infected_count = len(initial_infected)
    states[initial_infected] = 1

    for t in range(timesteps):
        new_states = states.copy()
        for node in network.nodes():
            if states[node] == 0:  # Susceptible node
                neighbors = list(network.neighbors(node))
                infected_neighbors = sum(states[neighbors])
                if np.random.rand() < p * infected_neighbors:
                    new_states[node] = 1  # Node becomes infected
            else:  # Infected node
                if np.random.rand() < q:
                    new_states[node] = 0  # Node recovers
                    infected_count -= 1

        states = new_states.copy()

        # Stop simulation if no more infected nodes
        if infected_count == 0:
            break

    return states

def calculate_threshold(network, p_range, q, initial_infected, timesteps, num_simulations):
    threshold_results = []

    for p in p_range:
        total_final_infected = 0

        for _ in range(num_simulations):
            final_states = uau_sis_simulation(network, p, q, initial_infected, timesteps)
            total_final_infected += sum(final_states)

        avg_final_infected = total_final_infected / num_simulations
        threshold_results.append((p, avg_final_infected))

    return threshold_results

# 构建多路网络，这里使用简单的随机图作为示例
num_layers = 2
layer_size = 100
p_edge = 0.1
networks = []
for _ in range(num_layers):
    network = nx.erdos_renyi_graph(layer_size, p_edge)
    networks.append(network)

# 将多个网络叠加成多路网络
multiplex_network = nx.disjoint_union_all(networks)

# 设置参数和初始感染节点
p_range = np.linspace(0, 0.5, 6)
q = 0.1
initial_infected = [0, 1]
timesteps = 100
num_simulations = 100

# 计算传播阈值
threshold_results = calculate_threshold(multiplex_network, p_range, q, initial_infected, timesteps, num_simulations)

# 输出结果
for threshold in threshold_results:
    print("p:", threshold[0], "Average Final Infected:", threshold[1])