new file: .gitignore

modified:   anchor.py
	deleted:    anchors.txt

.gitignore

@@ -0,0 +1 @@
+result/

README.md

@@ -1,11 +1,8 @@
-需要的 python包: numpy
+需要的 python包: numpy, seaborn, matplotlib, scipy
 
 执行python anchor.py后，自动将计算出的m点保存到本地，需要四个参数：
-
 input_file 级联数据文件路径；
-
-output_file 计算出的m点保存路径；
-
+result_path 计算出的结果保存路径；
 anchor_budget m点预算；
-
 obs_time 可观测时间，默认为-1，可不填写；
+max_cas_num 处理的事件数量，默认为-1，可不填写；

anchor.py

@@ -1,7 +1,16 @@
 from collections import defaultdict
+import time
 import numpy as np
 import copy
 import argparse
+import json
+from scipy.optimize import curve_fit
+import matplotlib.pyplot as plt
+import numpy as np
+import time
+from matplotlib import font_manager
+import seaborn as sns
+import os
 
 def find_max_indices_numpy(L_dict):    
     keys_arr = np.array(list(L_dict.keys()))
@@ -73,9 +82,10 @@ def select_social_sensors(R, L_dict, user_event_dict, event_user_dict, b):
         
     
     print(f"Select social sensors finish! Get social sensors, num: {len(A)}")
-    return A
+    return A, all_cas
 
-def handle_cas(filename, obs_time=-1):
+def handle_cas(args):
+    filename, obs_time =  args.input_file,  args.obs_time
     print("Handle cascade begin!")
     user_event_dict = defaultdict(dict)
     event_user_dict = defaultdict(list)
@@ -84,8 +94,8 @@ def handle_cas(filename, obs_time=-1):
         for line in file:
             
             cascades_total += 1
-            # if cascades_total > 100:
-            #     break
+            if cascades_total > args.max_cas_num and args.max_cas_num != -1:
+                break
             parts = line.split(',')
             cascade_id = parts[0]
             activation_times = {}
@@ -128,31 +138,379 @@ def handle_cas(filename, obs_time=-1):
         
     return L_dict, user_event_dict, event_user_dict
 
-def generate_anchors(input_file, output_file, anchor_budget, obs_time = -1):
+def generate_anchors(args):
+    result_path, anchor_budget = args.result_path, args.anchor_budget
     
-    L_dict, user_event_dict, event_user_dict = handle_cas(input_file, obs_time)
+    L_dict, user_event_dict, event_user_dict = handle_cas(args)
     
     num_nodes = len(L_dict.keys())
-    max_anchor_num = int(num_nodes*0.02)
     
-    if anchor_budget > max_anchor_num:
+    anchor_num = 0
+    
+    if anchor_budget > 0.02:
+        max_anchor_num = int(num_nodes*0.02)
         print(f"Max anchor num is {max_anchor_num}, anchor_budget is set to {max_anchor_num}")
-        anchor_budget = max_anchor_num
-    
-    A = select_social_sensors(R, L_dict, user_event_dict, event_user_dict, anchor_budget)
+        anchor_num = max_anchor_num
+    else:
+        anchor_num = int(num_nodes*anchor_budget)
     
+    A, all_cas = select_social_sensors(R, L_dict, user_event_dict, event_user_dict, anchor_num)
+    os.makedirs(result_path, exist_ok=True)
+    output_file = os.path.join(result_path, f'anchors_{args.anchor_budget}.txt')
     with open(output_file, 'w') as file:
         for item in A:
             file.write(f"{item}\n")
+    return A, all_cas, user_event_dict
+
+# Logistic 函数定义
+def logistic(t, K, r, t0):
+    return K / (1 + np.exp(-r * (t - t0)))
+
+def find_inflection_logistic(t_list, c_list):
+    t_array = np.array(t_list)
+    c_array = np.array(c_list)
+
+    # 时间归一化到 [0, 1]
+    t_min = t_array.min()
+    t_max = t_array.max()
+    
+    if len(set(c_array)) < 3 or max(c_array) <= 1 or t_max - t_min == 0:
+        return None
+    t_array_normalized = (t_array - t_min) / (t_max - t_min)
+
+    try:
+        p0 = [max(c_array) if max(c_array) > 0 else 1.0, 1.0, 0.5] # 确保 K 初始值不为0，如果c_array全是0
+        
+        # 检查 c_array 是否有效
+        if max(c_array) <= 0: # 如果c_array都是0或负数，也无法拟合S曲线
+             print("警告: 累计值 (c_list) 没有正增长，无法拟合S曲线。")
+             return None
+
+        # 调整 bounds，如果 K 的下限是0，max(c_array)也可能是0，可能导致问题
+        # bounds = ([0, 0, 0], [np.inf, 10, 1]) # K r t0
+        # K > 0 (所以下限设为很小的正数，或基于数据)
+        # r > 0 (增长率)
+        # t0 在 [0, 1] 范围内
+        bounds = ([1e-6, 1e-6, 0], [np.inf, 10, 1]) # K, r 应该为正
+
+        popt, _ = curve_fit(logistic, t_array_normalized, c_array, p0=p0, bounds=bounds, maxfev=10000)
+        K, r, t0 = popt
+        return t0
+    except (RuntimeError, ValueError):
+        print(f"警告: 曲线拟合失败或参数无效")
+        return None
+
+# 获取所有事件的爆发时间
+def get_t0(args):
+    input_file, result_path = args.input_file, args.result_path
+    
+    print("To get All Cas' T0")
+    os.makedirs(result_path, exist_ok=True)
+    json_file = os.path.join(result_path, 'prepare.json')
+    prepare = {}
+    if os.path.exists(json_file):
+        print(f"文件 {json_file} 存在。")
+        with open(json_file, 'r', encoding='utf-8') as f:
+            prepare = json.load(f)
+        return prepare
+    t0_dict = {}
+    num_time_points = 50  # 你希望在平均趋势图上采样的点数，可以调整
+    common_normalized_timeline = np.linspace(0, 1, num_time_points)
+    nodes_in_bins = {i: 0 for i in range(len(common_normalized_timeline) - 1)}
+
+    interpolated_counts_all_cascades = []
+    cascades_total = 0
+    t0_mean = 0
+    with open(input_file) as file:
+        for line in file:
+            cascades_total += 1
+            if cascades_total > args.max_cas_num and args.max_cas_num != -1:
+                break
+            parts = line.split(',')
+            cascade_id = parts[0]
+
+            paths = parts[1:]
+            t_max = 0
+            t_min = float('inf')
+            cas_set = set()
+            times = []
+            counts = []
+            for p in paths:
+                nodes = p.split(':')[0].split('/')
+                time_now = int(p.split(':')[1])
+                if time_now > t_max:
+                    t_max = time_now
+                if time_now < t_min:
+                    t_min = time_now
+                node = nodes[-1]
+                node_id = int(node)
+                cas_set.add(node_id) 
+                times.append(time_now)
+                counts.append(len(cas_set))
+            
+            sorted_times = sorted(times)
+            if t_max - t_min <= 0:
+                continue
+            nodes_in_bin = {i: 0 for i in range(len(common_normalized_timeline) - 1)}
+            for i in range(len(times)):
+                participation_time = (sorted_times[i] - t_min)/(t_max - t_min)
+                sorted_times[i] = participation_time
+                bin_index = np.digitize(participation_time, common_normalized_timeline, right=False) - 1
+                if bin_index == -1 and participation_time == common_normalized_timeline[0]:
+                    bin_index = 0
+                elif bin_index == len(common_normalized_timeline) - 1:
+                    if participation_time == common_normalized_timeline[-1]:
+                        bin_index = len(common_normalized_timeline) - 2
+                    else:
+                        continue
+                if 0 <= bin_index < len(nodes_in_bin):
+                    nodes_in_bins[bin_index] +=1
+                    nodes_in_bin[bin_index] += 1
+            
+            interpolated_counts = np.interp(common_normalized_timeline, 
+                                        sorted_times, 
+                                        counts)
+            estimated_increments_between_common_times = np.diff(interpolated_counts)
+            interpolated_counts_all_cascades.append(estimated_increments_between_common_times)
+            
+            t0 = find_inflection_logistic(sorted_times, counts)
+            if t0 is None:
+                continue
+            t0_dict[cascade_id] = t0
+            t0_mean += t0
+            
+            
+            
+    t0_mean /= len(t0_dict)
+    stacked_interpolated_counts = np.vstack(interpolated_counts_all_cascades).tolist()
+    counts_0 = np.array([nodes_in_bins[i] for i in sorted(nodes_in_bins.keys())])
+    all_counts = (counts_0 / counts_0.max()).tolist()
+    
+    
+    prepare['t0'] = t0_dict
+    prepare['t0_mean'] = t0_mean
+    prepare['interpolated_counts_sum'] = stacked_interpolated_counts
+    prepare['interpolated_counts_step_avg'] = all_counts
+    
+    try:
+        with open(json_file, 'w', encoding='utf-8') as f:
+            json.dump(prepare, f, indent=4, ensure_ascii=False)
+        print(f"t0_dict已成功保存到 JSON 文件: {json_file}")
+    except TypeError as e:
+        print(f"错误: 字典中可能包含无法序列化为 JSON 的数据类型。错误: {e}")
+    except IOError:
+        print(f"错误: 无法打开或写入文件 '{json_file}'。")
+    except Exception as e:
+        print(f"保存 JSON 文件时发生未知错误: {e}")
+        
+    return prepare
+                
+
+def analyse_result(node_list, user_event_dict, prepare, args):
+    result_path = args.result_path
+    t0_dict = prepare['t0']
+    t0_mean = prepare['t0_mean']
+    anchors_activation_times_before_t0 = defaultdict(list)
+    anchors_activation_times = defaultdict(list)
+    cas_set = set()
+    for k,v in user_event_dict.items():
+        if k in node_list:
+            for cas_id, ac_time in v.items():
+                cas_set.add(cas_id)
+                if cas_id not in t0_dict:
+                    continue
+                anchors_activation_times_before_t0[k].append(ac_time - t0_dict[cas_id])
+                anchors_activation_times[k].append(ac_time)
+    
+    anchors_avg_activation_time_before_t0 = dict()
+    anchors_avg_activation_time = dict()
+    
+    for node_id, times in anchors_activation_times_before_t0.items():
+        if times:
+            anchors_avg_activation_time_before_t0[node_id] = (sum(times) / len(times))
+        else:
+            anchors_avg_activation_time_before_t0[node_id] = None
+    
+    for node_id, times in anchors_activation_times.items():
+        if times:
+            anchors_avg_activation_time[node_id] = (sum(times) / len(times))
+        else:
+            anchors_avg_activation_time[node_id] = None
+            
+    anchors_total_avg_time_before_t0 = sum(t for t in anchors_avg_activation_time_before_t0.values() if t is not None) / len(anchors_avg_activation_time_before_t0)
+    anchors_total_avg_time = sum(t for t in anchors_avg_activation_time.values() if t is not None) / len(anchors_avg_activation_time)
+
+    os.makedirs(result_path, exist_ok=True)
+    
+    anylyze_result = {}
+    anylyze_result['anchors_act_cas_num'] = len(cas_set)
+    anylyze_result['act_cas'] = list(cas_set)
+    anylyze_result['anchors_total_avg_time_before_t0'] = anchors_total_avg_time_before_t0
+    anylyze_result['anchors_avg_activation_time_before_t0'] = anchors_avg_activation_time_before_t0
+    anylyze_result['anchors_total_avg_time'] = anchors_total_avg_time
+    anylyze_result['anchors_avg_activation'] = anchors_avg_activation_time
+    
+    json_file = os.path.join(result_path, f'anylyze_result_{args.anchor_budget}.json')
+    try:
+        with open(json_file, 'w', encoding='utf-8') as f:
+            json.dump(anylyze_result, f, indent=4, ensure_ascii=False)
+        print(f"anylyze_result已成功保存到 JSON 文件: {json_file}")
+    except TypeError as e:
+        print(f"错误: 字典中可能包含无法序列化为 JSON 的数据类型。错误: {e}")
+    except IOError:
+        print(f"错误: 无法打开或写入文件 '{json_file}'。")
+    except Exception as e:
+        print(f"保存 JSON 文件时发生未知错误: {e}")
+    
+    print("开始绘制激活时间图 ")
+    # --- 绘制激活时间图 ---
+    important_times = [t for t in anchors_avg_activation_time_before_t0.values() if t is not None]
+    # 然后继续绘制图表
+    # 假设已经有 important_times 和 random_times
+    plt.figure(figsize=(10, 6))
+    sns.kdeplot(important_times, label='anchors', fill=True, color='red', linewidth=2)
+    
+    plt.title('Anchors activate time')
+    plt.xlabel('Time')
+    plt.ylabel('P')
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+
+    fig_file_1 = os.path.join(result_path, f'anchor_act_time_{args.anchor_budget}.png')
+    plt.savefig(fig_file_1,  # 文件名
+                dpi=300,          # 可选：分辨率 (dots per inch)
+                bbox_inches='tight',# 可选：尝试裁剪掉空白边缘
+               )
+    plt.close()
+    
+    print("开始绘制趋势图 ")
+    
+    # --- 绘制趋势图 ---
+    min_time = 0.0
+    max_time = 1 # 假设最大观察时间是 0.5，你可以根据你的数据调整
+    bin_width = 0.01
+    num_bins = int(np.ceil((max_time - min_time) / bin_width)) # 向上取整计算bin的数量
+
+    # 创建时间段的边界
+    # 例如：[0.0, 0.1, 0.2, 0.3, 0.4, 0.5]
+    time_bin_edges = np.linspace(min_time, max_time, num_bins + 1)
+    
+    nodes_in_bins = {i: 0 for i in range(len(time_bin_edges) - 1)}
+    for node, participation_time in anchors_avg_activation_time.items():
+        bin_index = np.digitize(participation_time, time_bin_edges, right=False) - 1
+        if bin_index == -1 and participation_time == time_bin_edges[0]:
+            bin_index = 0
+        elif bin_index == len(time_bin_edges) - 1:
+            if participation_time == time_bin_edges[-1]:
+                bin_index = len(time_bin_edges) - 2
+            else:
+                continue
+        if 0 <= bin_index < len(nodes_in_bins):
+            nodes_in_bins[bin_index] += 1
+    
+    
+    plt.figure(figsize=(10, 6))  
+    
+    bin_labels = []
+    for i in range(len(time_bin_edges) - 1):
+        start_time = time_bin_edges[i]
+        end_time = time_bin_edges[i+1]
+        # 你可以用区间的字符串表示，或者区间的中心点
+        bin_labels.append(f"[{start_time:.2f}-{end_time:.2f})")
+        # bin_labels.append(f"{start_time:.1f}-") # 更简洁的标签，只显示起始
+
+    # 获取每个 bin 的计数值
+    counts_0 = np.array([nodes_in_bins[i] for i in sorted(nodes_in_bins.keys())])
+    counts = (counts_0 / counts_0.max()).tolist()
+    # X 轴的位置 (用于条形图)
+    x_positions = time_bin_edges[:-1] + bin_width/2
+    bars = plt.bar(x_positions, counts, 
+               width=bin_width,        # 条形的宽度
+               color='skyblue',  # 条形的颜色
+               edgecolor='black') # 条形的边框颜色
+    # bar_index = 0
+    # for bar in bars:
+    #     yval = bar.get_height()
+    #     if yval > 0: # 只为非零的条形添加文本
+    #         plt.text(bar.get_x() + bar.get_width()/2.0, yval + 0.05, # 文本位置微调
+    #                 counts_0[bar_index],      
+    #                 ha='center',    # 水平居中
+    #                 va='bottom')    # 垂直对齐方式
+    #     bar_index +=1
+    
+    # stacked_interpolated_counts = np.array(prepare['interpolated_counts'])
+
+    # stacked_log_counts = stacked_interpolated_counts
+    # # 沿着级联的维度 (axis=0) 计算平均值
+    # average_counts_trend = np.mean(stacked_log_counts, axis=0) # (num_time_points,)
+    # max_counts = average_counts_trend.max()
+
+    # std_counts_trend = np.std(stacked_log_counts, axis=0)
+    # sem_counts_trend = std_counts_trend / np.sqrt( average_counts_trend.shape[0])
+    # # 计算95%置信区间 (使用1.96作为Z分数)
+    # confidence_interval_upper = average_counts_trend + 1.96 * sem_counts_trend
+    # confidence_interval_lower = average_counts_trend - 1.96 * sem_counts_trend
+    
+    # average_counts_trend_normalized = average_counts_trend / max_counts
+    # confidence_interval_upper_normalized = confidence_interval_upper / max_counts
+    # confidence_interval_lower_normalized = confidence_interval_lower / max_counts
+    # confidence_interval_lower_corrected = np.maximum(0, confidence_interval_lower_normalized)
+    
+    # common_normalized_timeline = np.linspace(0, 1, average_counts_trend.shape[0])
+    # plt.plot(common_normalized_timeline, average_counts_trend_normalized, label='Average Participation Trend', color='blue', linewidth=2)
+
+    # # (可选) 绘制置信区间或标准差区域
+    # plt.fill_between(common_normalized_timeline, 
+    #                 confidence_interval_lower_corrected, 
+    #                 confidence_interval_upper_normalized, 
+    #                 color='blue', alpha=0.2, label='95% Confidence Interval')
+    
+    # counts_step = prepare['interpolated_counts_step_avg']
+    # common_normalized_timeline = np.linspace(0, 1, len(counts_step))
+    # plt.plot(common_normalized_timeline, counts_step, label='Average Participation Trend', color='blue', linewidth=2)
+
+
+    # --- 使用 plt.axvline() 绘制垂直红线 ---
+    plt.axvline(x=t0_mean,  # 线的 x 坐标
+                color='red',              # 线的颜色
+                linestyle='--',           # 线型 (例如 'solid', '--', '-.', ':')
+                linewidth=2,              # 线的宽度
+                label=f'Average outbreak time = {t0_mean}') 
+    fig_file_2 = os.path.join(result_path, f'trend_{args.anchor_budget}.png')
+    plt.savefig(fig_file_2,  # 文件名
+                dpi=300,          # 可选：分辨率 (dots per inch)
+                bbox_inches='tight',# 可选：尝试裁剪掉空白边缘
+               )
+    plt.close()
+    return
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Parameters')
     parser.add_argument('--input_file', default='./dataset_for_anchor.txt', type=str, help='Cascade file')
-    parser.add_argument('--output_file', default='./anchors.txt', type=str, help='Anchors save file')
-    parser.add_argument('--anchor_budget', default=100, type=int, help='Anchors num')
+    parser.add_argument('--result_path', default='./result/', type=str, help='result save path')
+    parser.add_argument('--anchor_budget', default=0.005, type=float, help='Anchors num')
+    parser.add_argument('--max_cas_num', default=-1, type=int, help='Cascade num')
     parser.add_argument('--obs_time', default=-1, type=int, help='Anchors observe time, default seeting is -1, meaning can observe all')
+    parser.add_argument('--key_node_json', default='./key_node.json', type=str, help='key_node_json save file')
     return parser.parse_args()
 
 if __name__ == '__main__':
     args = parse_args()
-    generate_anchors(args.input_file, args.output_file, args.anchor_budget, args.obs_time)
+    
+    #===========执行m点挖掘算法, 返回m点以及感知的级联数量=================
+    A, all_cas, user_event_dict = generate_anchors(args)
+    
+    # #===========读取其他算法挖掘重要节点=================
+    # with open(args.key_node_json, 'r', encoding='utf-8') as f:
+    #     key_node_json = json.load(f) # 从文件对象 f 加载 JSON
+    #     print("JSON 文件成功加载！")
+    #     key_node_json['res']
+    
+    #===========分析结果=================
+    print("开始分析结果")
+    prepare = get_t0(args)
+    analyse_result(A, user_event_dict, prepare, args)
+    
+    
+    

anchors.txt

@@ -1,100 +0,0 @@
-154624
-166401
-48133
-121862
-3083
-57356
-146451
-397331
-67102
-82463
-23074
-49186
-48167
-77357
-75822
-143413
-290884
-360517
-48199
-64077
-7250
-51293
-361568
-509538
-26212
-25221
-44166
-247942
-8841
-434324
-126616
-64158
-176812
-233648
-50356
-197814
-445625
-63161
-106701
-472794
-588514
-535781
-412904
-517867
-55029
-108281
-216314
-59135
-292613
-11527
-247568
-84756
-309528
-217373
-30500
-3366
-291121
-333115
-78139
-9026
-118597
-145744
-409428
-139100
-43877
-57711
-515444
-121205
-1917
-60286
-315281
-70552
-58265
-47514
-56728
-49565
-138654
-66976
-80291
-49575
-70058
-522157
-83390
-83394
-123331
-3524
-82897
-27604
-48086
-149465
-3034
-83419
-427485
-48094
-41956
-491493
-456174
-144371
-84982
-50168