import networkx as nx import random # 读入edge文件，构建图结构 graph = {} with open("./edge/football.edge", "r") as f: for line in f: u, v = line.strip().split() if u not in graph: graph[u] = [] if v not in graph: graph[v] = [] graph[u].append(v) graph[v].append(u) # 随机选择一个节点作为感染源 infected = random.choice(list(graph.keys())) print("Infected node:", infected) # 初始化每个节点的感染状态 status = {} for node in graph: status[node] = "S" status[infected] = "I" # SI模型参数 p = 0.5 # 感染过程 while True: changed = False for node in graph: if status[node] == "I": for neighbor in graph[node]: if status[neighbor] == "S" and random.random() < p: status[neighbor] = "I" changed = True if not changed: break # 输出每个节点的感染状态和邻居 for node in graph: print(node, status[node], graph[node])续写一个加入观测节点的溯源代码并可视化

G_tmp = nx.read_edgelist('../input/google-web-graph/web-Google.txt', create_using = nx.DiGraph)啥意思

其中，'../input/google-web-graph/web-Google.txt' 是文件路径，create_using = nx.DiGraph 表示创建一个有向图对象。这段代码的作用是将文件中的边列表读入内存，以便后续进行图分析和处理。

代码解读，import networkx as nx import pandas as pd import matplotlib.pyplot as plt import numpy as np import scipy as sp

- import networkx as nx：导入 networkx 库，它是一个用于复杂网络分析的Python库，可以方便地创建、操作和可视化各种类型的网络结构。 - import pandas as pd：导入 pandas 库，并将其命名为 pd，它是一...

import networkx as nx

import networkx as nx 是Python中用于导入networkx库的语句。networkx是一个用于创建、操作和分析复杂网络的Python库。它提供了许多用于构建和分析图形的工具和算法，包括图形可视化、图形布局、图形分析等。使用...

import networkx as nx # 构造一个弦图 G = nx.Graph() G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (4, 6), (5, 6)]) # 判断是否为弦图 print(nx.is_chordal(G)) # 输出 True # 获取最大团 cliques = nx.chordal_graph_cliques(G) max_clique = max(cliques, key=len) print(max_clique) # 输出 [3, 4, 5, 6] # 计算树宽 treewidth = nx.chordal_graph_treewidth(G) print(treewidth) # 输出 2

import networkx as nx # 构造一个弦图 G = nx.Graph() G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (4, 6), (5, 6)]) # 判断是否为弦图 print(nx.is_chordal(G)) # 输出 True # 获取最大团 ...

import networkx as nx import matplotlib.pyplot as plt # create graph object G = nx.Graph() # add nodes G.add_node('A') G.add_node('B') G.add_node('C') G.add_node('D') # add edges with weights G.add_edge('A', 'B', weight=5) G.add_edge('A', 'C', weight=7) G.add_edge('B', 'D', weight=6) G.add_edge('C', 'D', weight=3) # define node positions (optional) pos = {'A': (0, 0), 'B': (1, 1), 'C': (-1, 1), 'D': (0, 2)} # draw nodes and edges with labels nx.draw_networkx_nodes(G, pos) nx.draw_networkx_edges(G, pos) nx.draw_networkx_labels(G, pos) edge_labels = nx.get_edge_attributes(G, 'weight') nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels) plt.axis('off') plt.show()

import networkx as nx import matplotlib.pyplot as plt # 创建一个带权图 G = nx.Graph() # 添加节点 G.add_node('A') G.add_node('B') G.add_node('C') G.add_node('D') # 添加带权边 G.add_edge('A', 'B', ...

import networkx as nxfrom sklearn.model_selection import train_test_splitfrom networkx.algorithms.link_prediction import katz_similarityfrom sklearn.metrics import roc_auc_score, average_precision_score# 加载数据集edges = []with open("email-Eu-core.txt") as f: for line in f: if line.startswith("#"): continue edge = line.strip().split() edges.append((int(edge[0]), int(edge[1])))# 划分训练集和测试集train_edges, test_edges = train_test_split(edges, test_size=0.1, random_state=42)# 构建无向图graph = nx.Graph()graph.add_edges_from(train_edges)# 计算相似度katz_scores = katz_similarity(graph, max_l=200, beta=0.01)# 计算AUC值y_true = [1 if edge in test_edges else 0 for edge in graph.edges()]y_scores = [katz_scores[edge] for edge in graph.edges()]auc = roc_auc_score(y_true, y_scores)# 计算Precisionk = 100top_k_edges = sorted(graph.edges(), key=lambda x: katz_scores[x], reverse=True)[:k]y_true = [1 if edge in test_edges else 0 for edge in top_k_edges]y_scores = [katz_scores[edge] for edge in top_k_edges]precision = average_precision_score(y_true, y_scores)print("Katz Algorithm")print("AUC: {:.4f}".format(auc))print("Precision@{}: {:.4f}".format(k, precision))有什么问题

应该在 import networkx as nx 后添加一个换行符。另外，代码中的 katz_similarity() 函数调用中使用了一个 max_l 参数，这个参数是不必要的，可以直接去掉，因为该函数不支持传递该参数。最后，代码未定义...

import pandas as pd import networkx as nx import matplotlib # 读取Excel表格并转化为邻接矩阵 df = pd.read_excel('E:/Download/.Download/2023年杭州电子科技大学第十三届研究生数学建模竞赛题/HDUGMCM-2023-Problem-B：多无人车协同快递配送问题/附件/附件1.xlsx', header=None) adjacency_matrix = df.values[1:, 1:] # 创建无向图 G = nx.Graph() # 添加节点 for i in range(1, len(adjacency_matrix) + 1): G.add_node(i, pos=(df.values[i, 0], df.values[0, i])) # 添加边 for i in range(len(adjacency_matrix)): for j in range(i, len(adjacency_matrix)): if adjacency_matrix[i][j] > 0: G.add_edge(i+1, j+1, weight=adjacency_matrix[i][j]) # 绘制图形 pos = nx.get_node_attributes(G, 'pos') nx.draw(G, pos, with_labels=True, node_size=50) labels = nx.get_edge_attributes(G, 'weight') nx.draw_networkx_edge_labels(G, pos, edge_labels=labels) plt.show()代码改正

import networkx as nx import matplotlib.pyplot as plt # 读取Excel表格并转化为邻接矩阵 df = pd.read_excel('E:/Download/.Download/2023年杭州电子科技大学第十三届研究生数学建模竞赛题/HDUGMCM-2023-...

import networkx as nx ModuleNotFoundError: No module named 'networkx'

您遇到的问题是导入networkx模块时出现了ModuleNotFoundError，这通常是由于缺少相应的依赖项导致的。根据您提供的引用内容，解决这个问题的方法有两种：方法一：通过命令行安装缺失的依赖项 1. 关闭解释器，打开...

import networkx as nx from neo4j import GraphDatabase from torch_geometric.data import Data # Connect to Neo4j database driver = GraphDatabase.driver(uri, auth=(username, password)) # Define a Cypher query to retrieve nodes and relationships from Neo4j query = """ MATCH (n)-[r]->(m) RETURN id(n) AS source, id(m) AS target, type(r) AS edge_type, labels(n) AS source_labels, labels(m) AS target_labels, properties(n) AS source_props, properties(m) AS target_props """ # Execute the query and retrieve the results with driver.session() as session: results = session.run(query) # Convert the query results to a NetworkX graph graph = nx.MultiDiGraph() for record in results: graph.add_edge(record['source'], record['target'], key=record['edge_type'], source_labels=record['source_labels'], target_labels=record['target_labels'], source_props=record['source_props'], target_props=record['target_props']) # Convert the NetworkX graph to a PyTorch Geometric Data object x = [] edge_index = [] edge_attr = [] for node in graph.nodes(): node_attrs = [] for label in graph.nodes[node]['labels']: node_attrs.append(label) for prop in graph.nodes[node]['source_props']: node_attrs.append(prop) x.append(node_attrs) for source, target, data in graph.edges(keys=True, data=True): edge_index.append([source, target]) edge_attrs = [] for label in data['source_labels']: edge_attrs.append(label) for prop in data['properties']: edge_attrs.append(prop) edge_attr.append(edge_attrs) data = Data(x=torch.tensor(x), edge_index=torch.tensor(edge_index).t().contiguous(), edge_attr=torch.tensor(edge_attr))详细注释

import networkx as nx from neo4j import GraphDatabase from torch_geometric.data import Data # 连接到Neo4j数据库 driver = GraphDatabase.driver(uri, auth=(username, password)) # 定义一个Cypher查询语句...

ubuntu 中 import networkx as nx ModuleNotFoundError: No module named 'networkx'

在Ubuntu中，当出现"ModuleNotFoundError: No module named 'networkx'"错误时，通常表示没有安装networkx模块。要解决此问题，您可以通过以下步骤来安装networkx模块： 1. 打开终端。 2. 在终端中输入以下命令来...

def graph_from_csv(path): graph = nx.Graph(name="Heroic Social Network") with open(path, 'rb') as data: reader = csv.reader(data) for row in reader: graph.add_edge(*row) return graph graph = graph_from_csv(HERO_NETWORK) graph.order() graph.size()

这段代码定义了一个名为graph_from_csv的函数，以从CSV文件中读取边列表并返回一个NetworkX图对象。然后，代码使用graph_from_csv函数从CSV文件中读取边，并创建了一个名为graph的无向图对象： python ...

帮我修改一下这段代码的错误import networkx as nx G = nx.DiGraph() start = [1, 3, 5, 7] to = [2, 4, 6, 8] for j in range(len(start)): G.add_edge(start[j], to[j]) nx.draw(G, with_lables=True)

import networkx as nx G = nx.DiGraph() start = [1, 3, 5, 7] to = [2, 4, 6, 8] for j in range(len(start)): G.add_edge(start[j], to[j]) nx.draw(G, with_labels=True) # 你在原来的代码中写成了...

修改代码，将图形绘制成一个矩形布局：import matplotlib.pyplot as plt# 将图形绘制成一个圆形布局pos = nx.circular_layout(G)# 绘制图形的节点和边nx.draw_networkx_nodes(G, pos)nx.draw_networkx_edges(G, pos)# 添加节点标签node_labels = {node: str(node) for node in G.nodes()}nx.draw_networkx_labels(G, pos, node_labels)# 添加边权重标签edge_labels = {(u, v): str(d['weight']) for u, v, d in G.edges(data=True)}nx.draw_networkx_edge_labels(G, pos, edge_labels)# 显示图形plt.show()

import networkx as nx # 创建一个图形对象 G = nx.Graph() # 添加节点和边 G.add_edges_from([(1, 2), (1, 3), (2, 3), (2, 4), (3, 4), (4, 5), (5, 6)]) # 生成矩形布局 pos = nx.spring_layout(G, k=0.5, ...

import matplotlib.pyplot as plt# 将图形绘制成一个圆形布局pos = nx.circular_layout(G)# 绘制图形的节点和边nx.draw_networkx_nodes(G, pos)nx.draw_networkx_edges(G, pos)# 添加节点标签node_labels = {node: str(node) for node in G.nodes()}nx.draw_networkx_labels(G, pos, node_labels)# 添加边权重标签edge_labels = {(u, v): str(d['weight']) for u, v, d in G.edges(data=True)}nx.draw_networkx_edge_labels(G, pos, edge_labels)# 显示图形plt.show() 修改代码，将图形绘制成一个矩形布局

import networkx as nx # 创建一个图形对象 G = nx.Graph() # 添加节点和边 G.add_edges_from([(1, 2), (1, 3), (2, 3), (2, 4), (3, 4), (4, 5), (5, 6)]) # 生成矩形布局 pos = nx.spring_layout(G, k=0.5, ...

import pandas as pd import networkx as nx import matplotlib.pyplot as plt df = pd.read_excel(r"C:\Users\li'yi'jie\Desktop\运筹学网络规划数据.xlsx") edges = [] for i in range(len(df)): edge = { "id": df.loc[i, "边的编号"], "tail": df.loc[i, "边的尾节点"], "head": df.loc[i, "边的头节点"], "length": df.loc[i, "长度"], "capacity": df.loc[i, "容量"] } edges.append(edge) plt.figure(figsize=(10,8)) G = nx.DiGraph() for edge in edges: G.add_edge(edge["tail"], edge["head"], weight=edge["length"]) all_pairs = dict(nx.all_pairs_dijkstra_path_length(G)) pos = nx.spring_layout(G) nx.draw(G, pos, with_labels=True) labels = nx.get_edge_attributes(G, "weight") nx.draw_networkx_edge_labels(G, pos, edge_labels=labels, label_pos=0.3) plt.show()修改代码将所有顶点对间最短路长度写入一个新的excel

import networkx as nx # 从Excel中读入边列表 df = pd.read_excel(r"C:\Users\li'yi'jie\Desktop\运筹学网络规划数据.xlsx") edges = [] for i in range(len(df)): edge = { "id": df.loc[i, "边的编号"], ...

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import networkx as nx import matplotlib.pyplot as plt # 创建图 G = nx.Graph()

G_tmp = nx.read_edgelist('../input/google-web-graph/web-Google.txt', create_using = nx.DiGraph)啥意思

代码解读，import networkx as nx import pandas as pd import matplotlib.pyplot as plt import numpy as np import scipy as sp

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ubuntu 中 import networkx as nx ModuleNotFoundError: No module named 'networkx'

def graph_from_csv(path): graph = nx.Graph(name="Heroic Social Network") with open(path, 'rb') as data: reader = csv.reader(data) for row in reader: graph.add_edge(*row) return graph graph = graph_from_csv(HERO_NETWORK) graph.order() graph.size()

帮我修改一下这段代码的错误import networkx as nx G = nx.DiGraph() start = [1, 3, 5, 7] to = [2, 4, 6, 8] for j in range(len(start)): G.add_edge(start[j], to[j]) nx.draw(G, with_lables=True)

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