g.number_of_edges()中文

函数名：g.number_of_edges()

函数作用：返回图g的边数。

函数参数：无。

返回值：整数，表示图g的边数。

例子：

import networkx as nx

G = nx.Graph()  # 创建一个空图
G.add_edge(1, 2)  # 添加一条边
G.add_edge(2, 3)  # 添加一条边
print(G.number_of_edges())  # 输出2，表示图G有2条边

注意：该函数适用于有向图和无向图。对于多重图，边的数量包括多条边的数量。

相关问题

import pandas as pd import numpy as np import networkx as nx # 读取Excel文件中的邻接矩阵 adjacency_matrix = pd.read_excel('output.xlsx', index_col=0) # 将邻接矩阵转换为numpy数组 adjacency_matrix = adjacency_matrix.to_numpy() # 创建有向图对象 G = nx.DiGraph(adjacency_matrix) def preprocess(G): p = 0 directedGraph = nx.DiGraph() for u in G.nodes(): for v in G.neighbors(u): if (v != u): # propProb = G.number_of_edges(u, v) / G.in_degree(v) propProb = G.number_of_edges(u, v) / G.degree(v) directedGraph.add_edge(u, v, pp=propProb) # p += propProb # print(propProb) # print('平均阈值：', p/2939) return directedGraph def simulate(G, seedNode, propProbability): newActive = True currentActiveNodes = copy.deepcopy(seedNode) newActiveNodes = set() activatedNodes = copy.deepcopy(seedNode) # Biar ga keaktivasi 2 kali influenceSpread = len(seedNode) while (newActive): for node in currentActiveNodes: for neighbor in G.neighbors( node): # Harus dicek udah aktif apa belom, jangan sampe ngaktifin yang udah aktif if (neighbor not in activatedNodes): if (G[node][neighbor]['pp'] > propProbability): # flipCoin(propProbability) newActiveNodes.add(neighbor) activatedNodes.append(neighbor) influenceSpread += len(newActiveNodes) if newActiveNodes: currentActiveNodes = list(newActiveNodes) newActiveNodes = set() else: newActive = False # print("activatedNodes",len(activatedNodes),activatedNodes) return influenceSpread def flipCoin(probability): return random.random() < probability解释一下这个代码

这代码是一个传播模型的实现，用于模拟信息在一个有向图中的传播过程。首先，它读取一个Excel文件，其中包含了一个邻接矩阵，表示图中节点之间的连接关系。然后，将邻接矩阵转换为numpy数组，并创建一个有向图对象。

preprocess函数用于预处理图对象，它遍历所有节点，并计算每条边的传播概率（propProbability），然后将这些边添加到有向图中。

simulate函数用于模拟信息的传播过程。它接受一个种子节点（seedNode）和传播概率（propProbability）作为输入。通过迭代算法，不断将新激活的节点加入到currentActiveNodes集合中，并计算影响范围（influenceSpread）。直到没有新激活的节点时，传播过程结束。

最后，flipCoin函数用于模拟抛硬币的过程，以给定的概率返回True或False。在simulate函数中，它用于判断节点是否被激活。

总体上，这段代码实现了一个简单的信息传播模型，并可以根据传播概率和种子节点模拟信息在有向图中的传播过程。

import pandas as pd import numpy as np import networkx as nx import matplotlib.pyplot as plt # 读取Excel文件中的邻接矩阵 adjacency_matrix = pd.read_excel('output.xlsx', index_col=0) # 将邻接矩阵转换为numpy数组 adjacency_matrix = adjacency_matrix.to_numpy() # 创建有向图对象 G = nx.DiGraph(adjacency_matrix) def preprocess(G): p = 0 directedGraph = nx.DiGraph() for u in G.nodes(): for v in G.neighbors(u): if (v != u): propProb = G.number_of_edges(u, v) / G.degree(v) directedGraph.add_edge(u, v, pp=propProb) return directedGraph def simulate(G, seedNode, propProbability): newActive = True currentActiveNodes = seedNode.copy() newActiveNodes = set() activatedNodes = seedNode.copy() influenceSpread = len(seedNode) while newActive: for node in currentActiveNodes: for neighbor in G.neighbors(node): if neighbor not in activatedNodes: if G[node][neighbor]['pp'] > propProbability: newActiveNodes.add(neighbor) activatedNodes.append(neighbor) influenceSpread += len(newActiveNodes) if newActiveNodes: currentActiveNodes = list(newActiveNodes) newActiveNodes = set() else: newActive = False return influenceSpread def flipCoin(probability): return np.random.random() < probability # 可视化传播过程 def visualizePropagation(G, seedNode, propProbability): pos = nx.spring_layout(G) # 选择布局算法 labels = {node: node for node in G.nodes()} # 节点标签为节点名 colors = ['r' if node in seedNode else 'b' for node in G.nodes()] # 种子节点为红色，其他节点为蓝色 plt.figure(figsize=(10,6)) nx.draw_networkx_nodes(G, pos, node_color=colors) nx.draw_networkx_edges(G, pos) nx.draw_networkx_labels(G, pos, labels) plt.title('Propagation Visualization') plt.show() # 示例用法 seedNode = [7,36,17] propProbability = 0.7 directedGraph = preprocess(G) influenceSpread = simulate(directedGraph, seedNode, propProbability) print("Influence Spread:", influenceSpread) visualizePropagation(directedGraph, seedNode, propProbability)修改这个代码使得输出图形节点之间间隔合理能够看清

你可以尝试修改`nx.spring_layout()`函数的参数，例如`k`，来调整节点之间的距离。这个参数控制着节点之间的斥力大小，较大的`k`值会使节点之间的距离增大，从而使得图形更容易观察。

下面是修改后的代码示例：

# 可视化传播过程
def visualizePropagation(G, seedNode, propProbability):
    pos = nx.spring_layout(G, k=0.5)  # 调整k值来改变节点之间的距离
    labels = {node: node for node in G.nodes()}  # 节点标签为节点名
    colors = ['r' if node in seedNode else 'b' for node in G.nodes()]  # 种子节点为红色，其他节点为蓝色

    plt.figure(figsize=(10,6))
    nx.draw_networkx_nodes(G, pos, node_color=colors)
    nx.draw_networkx_edges(G, pos)
    nx.draw_networkx_labels(G, pos, labels)
    plt.title('Propagation Visualization')
    plt.show()

# 示例用法
seedNode = [7,36,17]
propProbability = 0.7

directedGraph = preprocess(G)
influenceSpread = simulate(directedGraph, seedNode, propProbability)
print("Influence Spread:", influenceSpread)

visualizePropagation(directedGraph, seedNode, propProbability)

通过调整`k`值，你可以改变节点之间的距离，使得图形更易于观察。可以尝试不同的值来达到最合适的效果。