code_twit-ReNew.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44cc2362-711c-4802-9f39-dc277ca8eef4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import json\n",
    "import community as community_louvain\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "import infomap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fa10b95-5da1-48f3-bf75-be17daab1a93",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def read_directed_graph(file_path, skip_lines):\n",
    "    G = nx.DiGraph()  # Initialize an empty directed graph\n",
    "    with open(file_path, 'r') as file:\n",
    "        for _ in range(skip_lines):\n",
    "            next(file)  # Skip the initial descriptive lines\n",
    "        for line in file:\n",
    "            parts = line.strip().split()\n",
    "            if len(parts) == 2:  # Only consider lines with exactly two node identifiers\n",
    "                u, v = parts\n",
    "                G.add_edge(u, v)\n",
    "    \n",
    "    return G\n",
    "\n",
    "\n",
    "path = '/Users/qing/Desktop/24s1/comp8880/project/code/data/soc/soc-twitter-follows-mun.edges'\n",
    "G = read_directed_graph(path,0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4086036-d6a3-49db-9caf-ed2b8ca75784",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(465017, 834798)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_nodes = G.number_of_nodes()\n",
    "num_edges = G.number_of_edges()\n",
    "\n",
    "num_nodes, num_edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1d5ceb4-7547-42ac-b1c5-3cdab3c6f9f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def leader_rank(G2, damping=0.85, tol=1e-6, max_iter=100):\n",
    "    # Add the ground node connected to all other nodes\n",
    "    ground_node = 'ground'\n",
    "    G = G2.copy()\n",
    "    G.add_node(ground_node)\n",
    "    for node in G2.nodes():  # Connect the ground node to all other nodes\n",
    "        G.add_edge(ground_node, node)\n",
    "        G.add_edge(node, ground_node)  # Assuming undirected graph, add both directions\n",
    "\n",
    "    # Create a mapping from node to index\n",
    "    node_list = list(G.nodes())\n",
    "    node_index = {node: i for i, node in enumerate(node_list)}\n",
    "    n = len(node_list)\n",
    "    \n",
    "    # Initialize scores to 1 for all nodes\n",
    "    scores = np.ones(n)\n",
    "    \n",
    "    # Create adjacency matrix\n",
    "    row = []\n",
    "    col = []\n",
    "    data = []\n",
    "    for node in G.nodes():\n",
    "        neighbors = list(G.neighbors(node))\n",
    "        if neighbors:\n",
    "            degree = len(neighbors)\n",
    "            for neighbor in neighbors:\n",
    "                row.append(node_index[neighbor])\n",
    "                col.append(node_index[node])\n",
    "                data.append(1.0 / degree)\n",
    "    \n",
    "    adjacency_matrix = csr_matrix((data, (row, col)), shape=(n, n))\n",
    "    \n",
    "    # Power iteration: iterate until convergence\n",
    "    for _ in range(max_iter):\n",
    "        new_scores = adjacency_matrix.dot(scores)\n",
    "        if np.linalg.norm(new_scores - scores, 1) < tol:\n",
    "            break\n",
    "        scores = new_scores\n",
    "    \n",
    "    # Remove the ground node score and normalize the scores\n",
    "    scores = np.delete(scores, node_index[ground_node])\n",
    "    total_score = np.sum(scores)\n",
    "    scores /= total_score\n",
    "    \n",
    "    # Create the final scores dictionary\n",
    "    final_scores = {node_list[i]: scores[i] for i in range(n) if node_list[i] != ground_node}\n",
    "    \n",
    "    return final_scores\n",
    "\n",
    "\n",
    "def calculate_h_index(G):\n",
    "    h_index = {}\n",
    "    for node in G.nodes():\n",
    "        degrees = [G.degree(neighbor) for neighbor in G.neighbors(node)]\n",
    "        degrees.sort(reverse=True)\n",
    "        h = 0\n",
    "        for i, degree in enumerate(degrees):\n",
    "            if degree >= i + 1:\n",
    "                h = i + 1\n",
    "            else:\n",
    "                break\n",
    "        h_index[node] = h\n",
    "    return h_index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62cc0d89-e362-4eb0-83bd-8f4dedbce2f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "degree_centrality = nx.degree_centrality(G)\n",
    "pagerank = nx.pagerank(G, alpha=0.85, personalization=None)\n",
    "leaderRank = leader_rank(G)\n",
    "hIndex = calculate_h_index(G)\n",
    "kShell = nx.core_number(G)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "256e4d0d-5f5c-4eb0-8da8-780349a347c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_centrality = sorted(degree_centrality.items(), key=lambda item: item[1], reverse=True)\n",
    "sorted_pagerank =  sorted(pagerank.items(), key=lambda item: item[1], reverse=True)\n",
    "sorted_leaderRank =  sorted(leaderRank.items(), key=lambda item: item[1], reverse=True)\n",
    "sorted_hIndex =  sorted(hIndex.items(), key=lambda item: item[1], reverse=True)\n",
    "sorted_kShell=  sorted(kShell.items(), key=lambda item: item[1], reverse=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7179d82e-988f-4b1b-9bb9-9a844dc613c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================================\n",
      "  Infomap v2.7.1 starts at 2024-05-18 14:56:27\n",
      "  -> Input network: \n",
      "  -> No file output!\n",
      "=======================================================\n",
      "  -> Ordinary network input, using the Map Equation for first order network flows\n",
      "Calculating global network flow using flow model 'undirected'... \n",
      "  -> Using undirected links.\n",
      "  => Sum node flow: 1, sum link flow: 1\n",
      "Build internal network with 465017 nodes and 834798 links...\n",
      "  -> One-level codelength: 15.4855172\n",
      "\n",
      "================================================\n",
      "Trial 1/1 starting at 2024-05-18 14:56:28\n",
      "================================================\n",
      "Two-level compression: 33% 0.49% 0.00238975026% 0.0292713174% \n",
      "Partitioned to codelength 4.2884848 + 5.97550254 = 10.26398734 in 2179 (2174 non-trivial) modules.\n",
      "Super-level compression: 0.45026712% to codelength 10.21777198 in 52 top modules.\n",
      "\n",
      "Recursive sub-structure compression: 27.4963772% 0.0292279525% 0% "
     ]
    }
   ],
   "source": [
    "# 创建节点映射，将节点字符串映射为整数，同时创建反向映射\n",
    "node_map = {node: i for i, node in enumerate(G.nodes())}\n",
    "reverse_node_map = {i: node for node, i in node_map.items()}  # 反向映射\n",
    "\n",
    "# 初始化 Infomap\n",
    "im = infomap.Infomap()\n",
    "\n",
    "# 将网络数据添加到 Infomap 对象，确保节点ID是整数\n",
    "for edge in G.edges():\n",
    "    source, target = edge\n",
    "    im.add_link(node_map[source], node_map[target])\n",
    "\n",
    "# 运行 Infomap 算法\n",
    "im.run()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "90baf5ca-fa19-42b6-8361-c53816c28839",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 提取社区分区结果，直接使用反向映射\n",
    "partition_infomap = {}\n",
    "for node in im.tree:\n",
    "    if node.is_leaf:\n",
    "        original_node = reverse_node_map[node.node_id]  # 直接使用反向映射查找原始节点标识符\n",
    "        partition_infomap[original_node] = node.module_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4ad9f69-bcd3-4294-97fd-4c4265afe7a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def community_aware_ranking_Baseline(G, partition, node_centrality, budget=None):\n",
    "    # Organize nodes by community and sort them within each community based on their centrality\n",
    "    community_nodes = {}\n",
    "    for node, comm in partition.items():\n",
    "        if comm not in community_nodes:\n",
    "            community_nodes[comm] = []\n",
    "        community_nodes[comm].append(node)\n",
    "\n",
    "    for comm in community_nodes:\n",
    "        community_nodes[comm].sort(key=lambda x: node_centrality[x], reverse=True)\n",
    "\n",
    "    # Select nodes from communities ensuring distribution across communities\n",
    "    ranked_nodes = []\n",
    "    if budget is None:\n",
    "        budget = len(G)\n",
    "    \n",
    "    while budget > 0 and any(community_nodes.values()):\n",
    "        for comm in sorted(community_nodes, key=lambda x: len(community_nodes[x]), reverse=True):\n",
    "            if community_nodes[comm]:\n",
    "                ranked_nodes.append(community_nodes[comm].pop(0))\n",
    "                budget -= 1\n",
    "                if budget == 0:\n",
    "                    break\n",
    "    \n",
    "    return ranked_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "186bd0d4-6851-4939-9a0e-ddfac24e3367",
   "metadata": {},
   "outputs": [],
   "source": [
    "def community_aware_ranking_ours_v5(G, partition, incommunity, communityImportance, total_budget):\n",
    "\n",
    "    communities = {}\n",
    "    for node, comm_id in partition.items():\n",
    "        if comm_id not in communities:\n",
    "            communities[comm_id] = []\n",
    "        communities[comm_id].append(node)\n",
    "\n",
    "    community_scores = {}\n",
    "    for comm_id, nodes in communities.items():\n",
    "        core_score = np.mean([communityImportance[node] for node in nodes])  # 计算该社区的平均k-shell得分\n",
    "        size_score = len(nodes)  # 获取社区的大小\n",
    "        community_scores[comm_id] = size_score * core_score**1.5\n",
    "    #print(\"Community Scores:\", community_scores)\n",
    "\n",
    "    # 使用线性归一化来分配预算\n",
    "    total_score = sum(community_scores.values())\n",
    "    community_budgets = {comm: int((score / total_score) * total_budget) for comm, score in community_scores.items()}\n",
    "\n",
    "   # print(community_budgets)\n",
    "    # 选择节点，考虑节点间的距离阈值\n",
    "    selected_nodes = set()\n",
    "    for comm_id in communities:\n",
    "        community_nodes = communities[comm_id]\n",
    "        budget = community_budgets[comm_id]\n",
    "        selected_for_comm = []\n",
    "\n",
    "        # 按度中心性排序\n",
    "        nodes_sorted = sorted(community_nodes, key=lambda n: incommunity[n], reverse=True)\n",
    "\n",
    "        for node in nodes_sorted:\n",
    "            if len(selected_for_comm) < budget:\n",
    "                selected_for_comm.append(node)\n",
    "                selected_nodes.add(node)\n",
    "                    \n",
    "    return selected_nodes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6cdfa97b-b8af-4419-91cc-42935875cd9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "indegree_centrality = nx.in_degree_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "de2e934a-f243-42c6-a3bd-6617b1116097",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_indegree_centrality = sorted(indegree_centrality.items(), key=lambda item: item[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "34aa16f6-f984-435d-996c-249abc929e91",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ranked Nodes: {'72332', '14654', '22407', '12688', '56491', '60455', '14187', '30767', '13913', '34378', '6679', '251760', '72570', '14378', '13854', '41752', '14141', '75153', '6389', '4979', '84381', '9296', '30636', '10269', '77453', '12965', '73992', '30922', '8781', '6595', '20596', '16759', '45065', '37448', '29674', '49072', '7209', '14764', '50268', '47898', '11338', '480', '20134', '25696', '81088', '44809', '6992', '32241', '12078', '13936', '13907', '170', '8828', '13116', '34234', '51898', '3580', '35837', '157436', '27597', '80238', '19692', '17314', '8846', '18228', '6699', '13922', '77437', '36844', '7011', '80029', '18166', '24678', '17291', '5074', '28538', '76585', '10541', '11634', '27843', '643', '6684', '11342', '18158', '28963', '8632'}\n",
      "Ranked informap Nodes: ['6389', '643', '75153', '41752', '6992', '7209', '56900', '129667', '34313', '2714', '13417', '46733', '14383', '63054', '2926', '55935', '28719', '66690', '50173', '84091', '262598', '21023', '306446', '44541', '44926', '46715', '91651', '181692', '39359', '79138', '81909', '40851', '31919', '19902', '35842', '44013', '100129', '288580', '97980', '13602', '37529', '129259', '188031', '238301', '350342', '251079', '10021', '41298', '274617', '216321', '42979', '62356', '16759', '3580', '81088', '29158', '277695', '84343', '78541', '114920', '65623', '4071', '30720', '222062', '256269', '20133', '46982', '94399', '18596', '67481', '40874', '240443', '262733', '72007', '312589', '41605', '163408', '71666', '266733', '270780', '215677', '154591', '104295', '8274', '72354', '84154', '317672', '37355', '153435', '247908', '73363', '19639', '207412', '107663', '43035', '170525', '53574', '43136', '57328', '49587']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "budget = 200\n",
    "\n",
    "\n",
    "ranked_nodes_ours = community_aware_ranking_ours_v5(G, partition_infomap, leaderRank,kShell, budget)\n",
    "ranked_nodes_baseline = community_aware_ranking_Baseline(G, partition_infomap, degree_centrality, budget)\n",
    "\n",
    "print(\"Ranked Nodes:\", ranked_nodes_ours)\n",
    "print(\"Ranked informap Nodes:\", ranked_nodes_baseline)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1c12e9ef-174e-4084-bf00-4248ddbb13df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def visualize_selected_nodes(G, partition, selected_nodes):\n",
    "    # 创建子图，只包括 selected_nodes 及其直接邻居\n",
    "    subgraph_nodes = set(selected_nodes)\n",
    "    for node in selected_nodes:\n",
    "        subgraph_nodes.update(G.neighbors(node))\n",
    "\n",
    "    subgraph = G.subgraph(subgraph_nodes)\n",
    "\n",
    "    # 为 selected_nodes 指定特殊颜色\n",
    "    node_colors = ['red' if node in selected_nodes else 'blue' for node in subgraph.nodes()]\n",
    "\n",
    "    # 使用更快的布局算法\n",
    "    pos = nx.kamada_kawai_layout(subgraph)  # Kamada-Kawai 布局通常比 spring 布局快\n",
    "    nx.draw(subgraph, pos, with_labels=False, node_color=node_colors, node_size=50)\n",
    "    plt.show()\n",
    "\n",
    "# 假设 G 是你的图对象，partition 是社区划分，selected_nodes 是选出的节点\n",
    "# visualize_selected_nodes(G, partition, selected_nodes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "93eb42bc-0c7a-43a6-a151-214c78f3e88b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[57], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mvisualize_selected_nodes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpartition_infomap\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mranked_nodes_ours\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[56], line 16\u001b[0m, in \u001b[0;36mvisualize_selected_nodes\u001b[0;34m(G, partition, selected_nodes)\u001b[0m\n\u001b[1;32m     13\u001b[0m node_colors \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m selected_nodes \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m subgraph\u001b[38;5;241m.\u001b[39mnodes()]\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# 使用更快的布局算法\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m pos \u001b[38;5;241m=\u001b[39m \u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkamada_kawai_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubgraph\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Kamada-Kawai 布局通常比 spring 布局快\u001b[39;00m\n\u001b[1;32m     17\u001b[0m nx\u001b[38;5;241m.\u001b[39mdraw(subgraph, pos, with_labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, node_color\u001b[38;5;241m=\u001b[39mnode_colors, node_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m     18\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/networkx/drawing/layout.py:709\u001b[0m, in \u001b[0;36mkamada_kawai_layout\u001b[0;34m(G, dist, pos, weight, scale, center, dim)\u001b[0m\n\u001b[1;32m    706\u001b[0m         pos \u001b[38;5;241m=\u001b[39m {n: pt \u001b[38;5;28;01mfor\u001b[39;00m n, pt \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(G, np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mlen\u001b[39m(G)))}\n\u001b[1;32m    707\u001b[0m pos_arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([pos[n] \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m G])\n\u001b[0;32m--> 709\u001b[0m pos \u001b[38;5;241m=\u001b[39m \u001b[43m_kamada_kawai_solve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdist_mtx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpos_arr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    711\u001b[0m pos \u001b[38;5;241m=\u001b[39m rescale_layout(pos, scale\u001b[38;5;241m=\u001b[39mscale) \u001b[38;5;241m+\u001b[39m center\n\u001b[1;32m    712\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(G, pos))\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/networkx/drawing/layout.py:727\u001b[0m, in \u001b[0;36m_kamada_kawai_solve\u001b[0;34m(dist_mtx, pos_arr, dim)\u001b[0m\n\u001b[1;32m    724\u001b[0m meanwt \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e-3\u001b[39m\n\u001b[1;32m    725\u001b[0m costargs \u001b[38;5;241m=\u001b[39m (np, \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m (dist_mtx \u001b[38;5;241m+\u001b[39m np\u001b[38;5;241m.\u001b[39meye(dist_mtx\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1e-3\u001b[39m), meanwt, dim)\n\u001b[0;32m--> 727\u001b[0m optresult \u001b[38;5;241m=\u001b[39m \u001b[43msp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    728\u001b[0m \u001b[43m    \u001b[49m\u001b[43m_kamada_kawai_costfn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    729\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpos_arr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    730\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mL-BFGS-B\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    731\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcostargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    732\u001b[0m \u001b[43m    \u001b[49m\u001b[43mjac\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    733\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    735\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m optresult\u001b[38;5;241m.\u001b[39mx\u001b[38;5;241m.\u001b[39mreshape((\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, dim))\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_minimize.py:696\u001b[0m, in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    693\u001b[0m     res \u001b[38;5;241m=\u001b[39m _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,\n\u001b[1;32m    694\u001b[0m                              \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n\u001b[1;32m    695\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m meth \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ml-bfgs-b\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 696\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[43m_minimize_lbfgsb\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjac\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    697\u001b[0m \u001b[43m                           \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    698\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m meth \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtnc\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    699\u001b[0m     res \u001b[38;5;241m=\u001b[39m _minimize_tnc(fun, x0, args, jac, bounds, callback\u001b[38;5;241m=\u001b[39mcallback,\n\u001b[1;32m    700\u001b[0m                         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions)\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_lbfgsb_py.py:359\u001b[0m, in \u001b[0;36m_minimize_lbfgsb\u001b[0;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)\u001b[0m\n\u001b[1;32m    353\u001b[0m task_str \u001b[38;5;241m=\u001b[39m task\u001b[38;5;241m.\u001b[39mtobytes()\n\u001b[1;32m    354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m task_str\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFG\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m    355\u001b[0m     \u001b[38;5;66;03m# The minimization routine wants f and g at the current x.\u001b[39;00m\n\u001b[1;32m    356\u001b[0m     \u001b[38;5;66;03m# Note that interruptions due to maxfun are postponed\u001b[39;00m\n\u001b[1;32m    357\u001b[0m     \u001b[38;5;66;03m# until the completion of the current minimization iteration.\u001b[39;00m\n\u001b[1;32m    358\u001b[0m     \u001b[38;5;66;03m# Overwrite f and g:\u001b[39;00m\n\u001b[0;32m--> 359\u001b[0m     f, g \u001b[38;5;241m=\u001b[39m \u001b[43mfunc_and_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    360\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m task_str\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNEW_X\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m    361\u001b[0m     \u001b[38;5;66;03m# new iteration\u001b[39;00m\n\u001b[1;32m    362\u001b[0m     n_iterations \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py:285\u001b[0m, in \u001b[0;36mScalarFunction.fun_and_grad\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    283\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray_equal(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx):\n\u001b[1;32m    284\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_x_impl(x)\n\u001b[0;32m--> 285\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    286\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_grad()\n\u001b[1;32m    287\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mf, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mg\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py:251\u001b[0m, in \u001b[0;36mScalarFunction._update_fun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_update_fun\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    250\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mf_updated:\n\u001b[0;32m--> 251\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_fun_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    252\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mf_updated \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py:155\u001b[0m, in \u001b[0;36mScalarFunction.__init__.<locals>.update_fun\u001b[0;34m()\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate_fun\u001b[39m():\n\u001b[0;32m--> 155\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mf \u001b[38;5;241m=\u001b[39m \u001b[43mfun_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py:137\u001b[0m, in \u001b[0;36mScalarFunction.__init__.<locals>.fun_wrapped\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnfev \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;66;03m# Send a copy because the user may overwrite it.\u001b[39;00m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;66;03m# Overwriting results in undefined behaviour because\u001b[39;00m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;66;03m# fun(self.x) will change self.x, with the two no longer linked.\u001b[39;00m\n\u001b[0;32m--> 137\u001b[0m fx \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;66;03m# Make sure the function returns a true scalar\u001b[39;00m\n\u001b[1;32m    139\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39misscalar(fx):\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_optimize.py:76\u001b[0m, in \u001b[0;36mMemoizeJac.__call__\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, \u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m     75\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\" returns the function value \"\"\"\u001b[39;00m\n\u001b[0;32m---> 76\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_if_needed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     77\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_value\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_optimize.py:70\u001b[0m, in \u001b[0;36mMemoizeJac._compute_if_needed\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(x \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjac \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(x)\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m---> 70\u001b[0m     fg \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     71\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjac \u001b[38;5;241m=\u001b[39m fg[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_value \u001b[38;5;241m=\u001b[39m fg[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/networkx/drawing/layout.py:744\u001b[0m, in \u001b[0;36m_kamada_kawai_costfn\u001b[0;34m(pos_vec, np, invdist, meanweight, dim)\u001b[0m\n\u001b[1;32m    741\u001b[0m pos_arr \u001b[38;5;241m=\u001b[39m pos_vec\u001b[38;5;241m.\u001b[39mreshape((nNodes, dim))\n\u001b[1;32m    743\u001b[0m delta \u001b[38;5;241m=\u001b[39m pos_arr[:, np\u001b[38;5;241m.\u001b[39mnewaxis, :] \u001b[38;5;241m-\u001b[39m pos_arr[np\u001b[38;5;241m.\u001b[39mnewaxis, :, :]\n\u001b[0;32m--> 744\u001b[0m nodesep \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    745\u001b[0m direction \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39meinsum(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mijk,ij->ijk\u001b[39m\u001b[38;5;124m\"\u001b[39m, delta, \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m (nodesep \u001b[38;5;241m+\u001b[39m np\u001b[38;5;241m.\u001b[39meye(nNodes) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1e-3\u001b[39m))\n\u001b[1;32m    747\u001b[0m offset \u001b[38;5;241m=\u001b[39m nodesep \u001b[38;5;241m*\u001b[39m invdist \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n",
      "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mnorm\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m/opt/anaconda3/lib/python3.9/site-packages/numpy/linalg/linalg.py:2541\u001b[0m, in \u001b[0;36mnorm\u001b[0;34m(x, ord, axis, keepdims)\u001b[0m\n\u001b[1;32m   2538\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m add\u001b[38;5;241m.\u001b[39mreduce(\u001b[38;5;28mabs\u001b[39m(x), axis\u001b[38;5;241m=\u001b[39maxis, keepdims\u001b[38;5;241m=\u001b[39mkeepdims)\n\u001b[1;32m   2539\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mord\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mord\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m   2540\u001b[0m     \u001b[38;5;66;03m# special case for speedup\u001b[39;00m\n\u001b[0;32m-> 2541\u001b[0m     s \u001b[38;5;241m=\u001b[39m (\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconj\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m)\u001b[38;5;241m.\u001b[39mreal\n\u001b[1;32m   2542\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m sqrt(add\u001b[38;5;241m.\u001b[39mreduce(s, axis\u001b[38;5;241m=\u001b[39maxis, keepdims\u001b[38;5;241m=\u001b[39mkeepdims))\n\u001b[1;32m   2543\u001b[0m \u001b[38;5;66;03m# None of the str-type keywords for ord ('fro', 'nuc')\u001b[39;00m\n\u001b[1;32m   2544\u001b[0m \u001b[38;5;66;03m# are valid for vectors\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "visualize_selected_nodes(G, partition_infomap, ranked_nodes_ours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4a33d2e1-f07b-4478-a787-442acee0d067",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7951988852020464\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def plot_sir_simulation(G, sorted_maps, top, infection_prob=0.05, steps=100):\n",
    "    plt.figure(figsize=(10, 5))\n",
    "\n",
    "    # Calculate average in-degree\n",
    "    avg_in_degree = sum(dict(G.in_degree()).values()) / G.number_of_nodes()\n",
    "    print(avg_in_degree)\n",
    "    recovery_prob = 1 / avg_in_degree\n",
    "\n",
    "    # Iterate through all sorted maps\n",
    "    for name, sorted_map in sorted_maps.items():\n",
    "        top_nodes = [node for node, _ in sorted_map[:top]]  # Extract top nodes\n",
    "        history, ever_infected, infected_counts = SIR_simulation(G, top_nodes, infection_prob, recovery_prob, steps)\n",
    "        plt.plot(infected_counts, marker='o', linestyle='-', label=f'{name}: Top {top}')\n",
    "\n",
    "    plt.title('Number of Ever Infected Nodes Over Time')\n",
    "    plt.xlabel('Time Steps')\n",
    "    plt.ylabel('Number of Ever Infected Nodes')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def SIR_simulation(G, initial_infected, infection_prob=0.1, recovery_prob=0.05, steps=100):\n",
    "    # Initialize the status of each node\n",
    "    status = {node: 'S' for node in G.nodes()}\n",
    "    for node in initial_infected:\n",
    "        status[node] = 'I'\n",
    "\n",
    "    history = []\n",
    "    ever_infected = set(initial_infected)\n",
    "    infected_counts = [len(ever_infected)]  # Initial number of infected nodes\n",
    "\n",
    "    # Simulation process\n",
    "    for _ in range(steps):\n",
    "        new_status = status.copy()\n",
    "        for node in G.nodes():\n",
    "            if status[node] == 'I':\n",
    "                # Determine neighbors based on whether the graph is directed\n",
    "                if G.is_directed():\n",
    "                    # For directed graphs, consider nodes that have edges towards 'node'\n",
    "                    neighbors = [n for n in G.predecessors(node)]\n",
    "                else:\n",
    "                    # For undirected graphs, consider all neighbors\n",
    "                    neighbors = list(G.neighbors(node))\n",
    "\n",
    "                # Attempt to infect neighbors\n",
    "                for neighbor in neighbors:\n",
    "                    if status[neighbor] == 'S' and random.random() < infection_prob:\n",
    "                        new_status[neighbor] = 'I'\n",
    "                        ever_infected.add(neighbor)\n",
    "                # Recovery process\n",
    "                if random.random() < recovery_prob:\n",
    "                    new_status[node] = 'R'\n",
    "        history.append(new_status)\n",
    "        status = new_status\n",
    "        infected_counts.append(len(ever_infected))  # Record the total number of infected nodes\n",
    "\n",
    "        if not any(s == 'I' for s in status.values()):\n",
    "            break\n",
    "\n",
    "    return history, ever_infected, infected_counts\n",
    "\n",
    "\n",
    "\n",
    "sorted_maps = {\n",
    "    \n",
    "     \"CommunityAwareOurs\": [(node, degree_centrality[node]) for node in ranked_nodes_ours],\n",
    "    \"CommunityAwareBaseline\": [(node, degree_centrality[node]) for node in ranked_nodes_baseline],\n",
    "    \"Centrality\": sorted_centrality,\n",
    "    #\"indegree_centrality\": sorted_indegree_centrality,\n",
    "    #\"PageRank\": sorted_pagerank,\n",
    "    \"LeaderRank\": sorted_leaderRank,\n",
    "   # \"H-index\": sorted_hIndex,\n",
    "    #\"K-Shell\": sorted_kShell\n",
    "}\n",
    "\n",
    "\n",
    "# 运行模拟并绘图\n",
    "plot_sir_simulation(G, sorted_maps,top=75, infection_prob=0.1,steps=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8e967d0a-90f8-489d-823f-19800183325d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# 统计前top节点在各社区中的分布\n",
    "def count_top_nodes_in_communities(G, sorted_maps, top, partition):\n",
    "    community_counts = {name: {} for name in sorted_maps.keys()}\n",
    "    \n",
    "    for name, sorted_map in sorted_maps.items():\n",
    "        top_nodes = [node for node, _ in sorted_map[:top]]  # 提取前top个节点\n",
    "        for node in top_nodes:\n",
    "            community = partition[node]\n",
    "            if community not in community_counts[name]:\n",
    "                community_counts[name][community] = 0\n",
    "            community_counts[name][community] += 1\n",
    "    \n",
    "    return community_counts\n",
    "\n",
    "# 可视化前top节点在各社区中的分布\n",
    "def plot_top_nodes_distribution(community_counts, top):\n",
    "    plt.figure(figsize=(15, 10))\n",
    "\n",
    "    for i, (name, counts) in enumerate(community_counts.items()):\n",
    "        communities = list(counts.keys())\n",
    "        counts = list(counts.values())\n",
    "        \n",
    "        plt.subplot(3, 3, i + 1)\n",
    "        plt.bar(communities, counts)\n",
    "        plt.title(f'Distribution of Top {top} Nodes in Communities\\n({name})')\n",
    "        plt.xlabel('Community')\n",
    "        plt.ylabel('Number of Top Nodes')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 统计每个社区的节点数量并排序\n",
    "def count_and_sort_communities(partition):\n",
    "    community_counts = {}\n",
    "    for node, community in partition.items():\n",
    "        if community not in community_counts:\n",
    "            community_counts[community] = 0\n",
    "        community_counts[community] += 1\n",
    "    \n",
    "    # 按照节点数量排序社区\n",
    "    sorted_community_counts = sorted(community_counts.items(), key=lambda x: x[1], reverse=True)\n",
    "    return sorted_community_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd273caa-053d-484e-8fb9-03c0df3aa2e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "top = 50\n",
    "\n",
    "community_counts = count_top_nodes_in_communities(G, sorted_maps, top, partition_infomap)\n",
    "plot_top_nodes_distribution(community_counts, top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "114fe246-d98a-4653-9e65-32c4ea50754e",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Community 1: 295193 nodes\n",
      "Community 2: 60190 nodes\n",
      "Community 3: 13863 nodes\n",
      "Community 4: 7743 nodes\n",
      "Community 5: 7161 nodes\n",
      "Community 6: 5882 nodes\n",
      "Community 8: 4794 nodes\n",
      "Community 7: 4654 nodes\n",
      "Community 11: 3582 nodes\n",
      "Community 9: 3572 nodes\n",
      "Community 10: 3254 nodes\n",
      "Community 12: 3205 nodes\n",
      "Community 13: 2930 nodes\n",
      "Community 15: 2671 nodes\n",
      "Community 14: 2665 nodes\n",
      "Community 18: 2645 nodes\n",
      "Community 17: 2479 nodes\n",
      "Community 16: 2409 nodes\n",
      "Community 20: 2335 nodes\n",
      "Community 19: 2137 nodes\n",
      "Community 21: 1846 nodes\n",
      "Community 24: 1784 nodes\n",
      "Community 25: 1716 nodes\n",
      "Community 23: 1581 nodes\n",
      "Community 22: 1572 nodes\n",
      "Community 26: 1542 nodes\n",
      "Community 32: 1526 nodes\n",
      "Community 27: 1376 nodes\n",
      "Community 29: 1360 nodes\n",
      "Community 30: 1329 nodes\n",
      "Community 28: 1252 nodes\n",
      "Community 33: 1228 nodes\n",
      "Community 31: 1202 nodes\n",
      "Community 34: 1156 nodes\n",
      "Community 35: 1136 nodes\n",
      "Community 37: 972 nodes\n",
      "Community 36: 939 nodes\n",
      "Community 39: 898 nodes\n",
      "Community 38: 751 nodes\n",
      "Community 40: 743 nodes\n",
      "Community 42: 694 nodes\n",
      "Community 41: 675 nodes\n",
      "Community 44: 673 nodes\n",
      "Community 45: 568 nodes\n",
      "Community 43: 540 nodes\n",
      "Community 47: 508 nodes\n",
      "Community 48: 507 nodes\n",
      "Community 46: 490 nodes\n",
      "Community 49: 418 nodes\n",
      "Community 50: 288 nodes\n",
      "Community 51: 215 nodes\n",
      "Community 52: 168 nodes\n"
     ]
    }
   ],
   "source": [
    "\n",
    "sorted_community_counts = count_and_sort_communities(partition_infomap)\n",
    "\n",
    "for community, count in sorted_community_counts:\n",
    "    print(f\"Community {community}: {count} nodes\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5556ae4d-c144-49bb-ad5b-2fc8d2387196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "社区 1 - 均值: 1.9944307622470723, 总和: 588742\n",
      "社区 2 - 均值: 1.7701611563382622, 总和: 106546\n",
      "社区 3 - 均值: 1.3428550818726106, 总和: 18616\n",
      "社区 4 - 均值: 1.3693658788583236, 总和: 10603\n",
      "社区 5 - 均值: 1.3944979751431363, 总和: 9986\n",
      "社区 6 - 均值: 1.4967698061883714, 总和: 8804\n",
      "社区 7 - 均值: 1.4860335195530727, 总和: 6916\n",
      "社区 8 - 均值: 1.3316645807259073, 总和: 6384\n",
      "社区 9 - 均值: 1.4997200447928332, 总和: 5357\n",
      "社区 10 - 均值: 1.503380454824831, 总和: 4892\n",
      "社区 11 - 均值: 1.3570630932439978, 总和: 4861\n",
      "社区 12 - 均值: 1.4043681747269892, 总和: 4501\n",
      "社区 13 - 均值: 1.4262798634812286, 总和: 4179\n",
      "社区 14 - 均值: 1.3223264540337711, 总和: 3524\n",
      "社区 15 - 均值: 1.3451890677648821, 总和: 3593\n",
      "社区 16 - 均值: 1.3669572436695725, 总和: 3293\n",
      "社区 17 - 均值: 1.2496974586526826, 总和: 3098\n",
      "社区 18 - 均值: 1.1402646502835538, 总和: 3016\n",
      "社区 19 - 均值: 1.301824988301357, 总和: 2782\n",
      "社区 20 - 均值: 1.2059957173447537, 总和: 2816\n",
      "社区 21 - 均值: 1.4723726977248104, 总和: 2718\n",
      "社区 22 - 均值: 1.4300254452926209, 总和: 2248\n",
      "社区 23 - 均值: 1.473750790638836, 总和: 2330\n",
      "社区 24 - 均值: 1.2690582959641257, 总和: 2264\n",
      "社区 25 - 均值: 1.3222610722610724, 总和: 2269\n",
      "社区 26 - 均值: 1.245136186770428, 总和: 1920\n",
      "社区 27 - 均值: 1.2957848837209303, 总和: 1783\n",
      "社区 28 - 均值: 1.4193290734824282, 总和: 1777\n",
      "社区 29 - 均值: 1.2448529411764706, 总和: 1693\n",
      "社区 30 - 均值: 1.3355906696764483, 总和: 1775\n",
      "社区 31 - 均值: 1.3893510815307821, 总和: 1670\n",
      "社区 32 - 均值: 1.067496723460026, 总和: 1629\n",
      "社区 33 - 均值: 1.1913680781758957, 总和: 1463\n",
      "社区 34 - 均值: 1.21280276816609, 总和: 1402\n",
      "社区 35 - 均值: 1.1126760563380282, 总和: 1264\n",
      "社区 36 - 均值: 1.2417465388711395, 总和: 1166\n",
      "社区 37 - 均值: 1.2016460905349795, 总和: 1168\n",
      "社区 38 - 均值: 1.2796271637816246, 总和: 961\n",
      "社区 39 - 均值: 1.0835189309576838, 总和: 973\n",
      "社区 40 - 均值: 1.2664872139973082, 总和: 941\n",
      "社区 41 - 均值: 1.2607407407407407, 总和: 851\n",
      "社区 42 - 均值: 1.2435158501440922, 总和: 863\n",
      "社区 43 - 均值: 1.424074074074074, 总和: 769\n",
      "社区 44 - 均值: 1.1411589895988112, 总和: 768\n",
      "社区 45 - 均值: 1.2658450704225352, 总和: 719\n",
      "社区 46 - 均值: 1.5857142857142856, 总和: 777\n",
      "社区 47 - 均值: 1.2401574803149606, 总和: 630\n",
      "社区 48 - 均值: 1.0611439842209074, 总和: 538\n",
      "社区 49 - 均值: 1.1172248803827751, 总和: 467\n",
      "社区 50 - 均值: 1.1701388888888888, 总和: 337\n",
      "社区 51 - 均值: 1.3581395348837209, 总和: 292\n",
      "社区 52 - 均值: 1.4880952380952381, 总和: 250\n"
     ]
    }
   ],
   "source": [
    "def calculate_community_kshell_stats(partition, kshell_scores):\n",
    "    \"\"\"\n",
    "    计算每个社区的k-shell值的均值和总值。\n",
    "\n",
    "    参数:\n",
    "    partition: 字典，节点到社区ID的映射。\n",
    "    kshell_scores: 字典，节点的k-shell得分。\n",
    "\n",
    "    返回:\n",
    "    community_stats: 字典，每个社区的k-shell均值和总和。\n",
    "    \"\"\"\n",
    "    # 组织每个社区的节点\n",
    "    communities = {}\n",
    "    for node, comm_id in partition.items():\n",
    "        if comm_id not in communities:\n",
    "            communities[comm_id] = []\n",
    "        communities[comm_id].append(node)\n",
    "\n",
    "    # 计算每个社区的k-shell均值和总和\n",
    "    community_stats = {}\n",
    "    for comm_id, nodes in communities.items():\n",
    "        kshell_values = [kshell_scores[node] for node in nodes]\n",
    "        mean_kshell = sum(kshell_values) / len(kshell_values) if kshell_values else 0\n",
    "        total_kshell = sum(kshell_values)\n",
    "        community_stats[comm_id] = {'mean': mean_kshell, 'total': total_kshell}\n",
    "\n",
    "    return community_stats\n",
    "\n",
    "community_stats = calculate_community_kshell_stats(partition_infomap, kShell)\n",
    "\n",
    "# 打印每个社区的统计数据\n",
    "for comm_id, stats in community_stats.items():\n",
    "    print(f\"社区 {comm_id} - 均值: {stats['mean']}, 总和: {stats['total']}\")\n"
   ]
  }
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