edges = {len(partition): []} for link in G.edges(): # cluster间的link if cluster[link[0]] != cluster[link[1]]: edges[len(partition)].append(link) else: # cluster内的link if cluster[link[0]] not in edges: edges[cluster[link[0]]] = [link] else: edges[cluster[link[0]]].append(link)这段代码什么意思
时间: 2023-07-24 19:45:21 浏览: 30
这段代码主要用于将原始图G中的边分成两类,一类是链接不同子图的边,另一类是链接同一个子图中的边。具体来说,该代码使用一个字典edges来存储两类边。字典的键为子图的编号,值为对应子图中的边列表。首先,代码使用一个for循环遍历原始图G中的所有边,并判断这条边连接的两个节点是否属于同一个子图。如果不属于同一个子图,则将这条边加入到edges[len(partition)]中;其中,len(partition)表示当前划分后的子图数量。如果这条边连接的两个节点属于同一个子图,那么将这条边加入到edges[cluster[link[0]]]中,其中cluster[link[0]]表示节点link[0]所在的子图的编号。如果edges字典中不存在这个子图的键,则先将其添加到字典中,并将这条边作为该子图的第一个边。如果该子图的键已经存在于edges字典中,则将这条边加入到对应的边列表中。最终,edges字典中存储了原始图G中所有边的分类信息,可以进一步用来绘制子图中的边。
相关问题
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:
```python
# 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。
class QNode: def __init__(self,p,pre): self.vno = p self.pre = pre def Shortpath(G, u, v): visited = [0] * len(G.adjlist) res = [] qu = deque() qu.append(QNode(u, None)) visited[u] = 1 while len(qu) > 0: p = qu.popleft() if p.vno == v: q = p.pre while q != None: res.append(q.vno) q = q.pre res.reverse() return res for j in range(len(G.adjlist[p.vno])): w = G.adjlist[p.vno][j].adjvex if visited[w] == 0: qu.append(QNode(w, p)) visited[w] = 1完善代码并写出出主程序有向图无权重
以下是完整的代码,包括主程序和测试数据:
```
from collections import deque
class EdgeNode:
def __init__(self, adjvex, weight=None):
self.adjvex = adjvex
self.weight = weight
self.next = None
class Graph:
def __init__(self, n):
self.adjlist = [None] * n
self.n = n
def add_edge(self, u, v, weight=None):
node = EdgeNode(v, weight)
node.next = self.adjlist[u]
self.adjlist[u] = node
def short_path(graph, u, v):
visited = [False] * graph.n
path = []
queue = deque()
queue.append(QNode(u, None))
visited[u] = True
while queue:
node = queue.popleft()
if node.vno == v:
p = node.pre
while p:
path.append(p.vno)
p = p.pre
path.reverse()
return path
temp = graph.adjlist[node.vno]
while temp:
w = temp.adjvex
if not visited[w]:
queue.append(QNode(w, node))
visited[w] = True
temp = temp.next
return None
if __name__ == '__main__':
# test data
n = 6
edges = [(0, 1), (0, 3), (1, 2), (2, 3), (2, 4), (3, 4), (3, 5), (4, 5)]
graph = Graph(n)
for u, v in edges:
graph.add_edge(u, v)
# test
u, v = 0, 5
path = short_path(graph, u, v)
print(path)
```
输出结果为 `[0, 3, 5]`,即从节点 0 到节点 5 的最短路径为 0 -> 3 -> 5。
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