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])之后续写一个加入观测节点的溯源代码并可视化

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])

其中，通过读取一个edge文件构建了一个图结构，然后随机选择一个节点作为感染源，初始化每个节点的感染状态为“S”（易感染状态）或“I”（感染状态）。接着，通过SI模型参数p来模拟感染过程，当一个节点处于感染...

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

# 添加节点 G.add_node(1) G.add_node(2) G.add_node(3) # 添加边 G.add_edge(1, 2) G.add_edge(2, 3) G.add_edge(3, 1) # 绘制图形 nx.draw(G, with_labels=True) plt.show()

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 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 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 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 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...

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

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...

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： ...

import networkx as nximport matplotlib.pyplot as plt# 读取人物关系数据with open('characters.txt', 'r') as f: data = f.readlines()# 创建空的Graph对象G = nx.Graph()# 添加节点和边for line in data: characters = line.strip().split(',') for i in range(len(characters)): if not G.has_node(characters[i]): G.add_node(characters[i]) for j in range(i+1, len(characters)): if not G.has_node(characters[j]): G.add_node(characters[j]) if not G.has_edge(characters[i], characters[j]): G.add_edge(characters[i], characters[j])# 绘制图形pos = nx.spring_layout(G)nx.draw(G, pos, with_labels=True, font_size=10, node_size=500, node_color='lightblue', edge_color='gray')plt.show()

这是Python中导入networkx和matplotlib.pyplot模块的语句。networkx是一个用于创建、操作和研究复杂网络的Python库，而matplotlib.pyplot是一个用于绘制图形的Python库。

import pandas as pd import numpy as np 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=(15,15)) 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) #nx.draw_networkx_edge_labels(G, pos, edge_labels=labels, label_pos=0.3) plt.show() # 使用Floyd算法求解所有顶点对之间的最短路长度 dist = nx.floyd_warshall(G) # 将结果写入Excel文件中 rows = [] for i, row in enumerate(dist): for j, value in enumerate(row): rows.append({"起始节点": i, "终止节点": j, "最短路长度": value}) df_result = pd.DataFrame(rows) df_result.to_excel(r"C:\Users\li'yi'jie\Desktop\最短路径结果.xlsx", index=False)报错TypeError: 'numpy.int64' object is not iterable如何修改？

# 将结果写入Excel文件中 rows = [] for i, row in enumerate(dist): for j, value in enumerate(list(row)): # 将row转换为列表类型 rows.append({"起始节点": i, "终止节点": j, "最短路长度": value}) df_...

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。

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 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) 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, "长度") nx.draw_networkx_edge_labels(G, pos, edge_labels=labels) plt.show()如何修改使生成的网络图的边旁标注权重？

可以在代码最后添加以下语句： ...其中 labels 变量用于获取图 G 中所有边的权重信息，nx.draw_networkx_edge_labels() 函数用于在图形中绘制边旁的权重标签。最后使用 plt.show() 函数显示图形即可。

import pandas as pd import networkx as nx # 读取数据 data = pd.read_excel('附件1.xlsx') # 构建有向图 G = nx.DiGraph() for _, row in data.iterrows(): source = row['发货城市'] target = row['收货城市'] weight = row['快递数量'] if not G.has_edge(source, target): G.add_edge(source, target, weight=weight) else: G[source][target]['weight'] += weight # 计算节点的入度和出度 in_degree = dict(G.in_degree(weight='weight')) out_degree = dict(G.out_degree(weight='weight')) # 归一化处理 total_in = sum(in_degree.values()) total_out = sum(out_degree.values()) for node in G.nodes(): if total_in > 0: in_degree[node] /= total_in if total_out > 0: out_degree[node] /= total_out # 计算 PageRank 值 pr = nx.pagerank(G, weight='weight') # 排序输出结果 result = sorted(pr.items(), key=lambda x: x[1], reverse=True)[:5] for i, (node, score) in enumerate(result): print(f'{i+1}. {node} ({score:.4f})')

这段代码是使用 Python 的 pandas 和 networkx 库读取 Excel 中的数据，构建有向图，并进行 PageRank 计算，最后输出 PageRank 值最高的前五个节点。具体步骤如下： 1. 读取 Excel 中的数据，存储在一个 pandas ...

(源码)基于QT框架的云存储系统.zip

# 基于QT框架的云存储系统 ## 项目简介本项目是一个基于QT框架开发的云存储系统，旨在为用户提供一个安全、高效的文件存储和分享平台。系统采用CS架构，客户端通过QT框架搭建，服务端运行在Centos 7环境下。用户可以通过系统进行文件的上传、下载、分享，以及与好友的私聊和文件分享。 ## 项目的主要特性和功能好友管理支持添加、删除好友，私聊好友，以及分享文件给好友。文件管理提供文件夹的创建、删除、移动、重命名操作，支持文件的上传、下载、移动和分享。用户界面使用QT框架搭建用户界面，提供友好的交互体验。网络通信通过自定义的交互协议实现客户端与服务器的高效数据交互。并发处理服务器端采用多路复用、内存池、线程池等技术，确保在并发环境下的稳定运行。 ## 安装使用步骤 1. 下载源码从项目仓库下载源码文件。 2. 配置开发环境服务端安装Centos 7，并配置vim、G++、gdb等开发工具。

2010-2023国自科立项名单管理学部.xlsx

1、资源内容地址：https://blog.csdn.net/2301_79696294/article/details/143636809 2、数据特点：今年全新，手工精心整理，放心引用，数据来自权威，且标注《数据来源》，相对于其他人的控制变量数据准确很多,适合写论文做实证用，不会出现数据造假问题 3、适用对象：大学生，本科生，研究生小白可用，容易上手！！！ 3、课程引用: 经济学，地理学，城市规划与城市研究，公共政策与管理，社会学，商业与管理

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