✨ 长期致力于复杂网络、不完全信息、链路预测、瓦解策略、链路预测漫画效应、精度评价、MATLAB GUI研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1基于特征谱的网络可预测性度量与漫画效应强度指标针对不完全信息下网络瓦解策略设计难题首先提出一种基于拉普拉斯矩阵特征谱分布熵的可预测性指标。计算网络前k个特征值的归一化间隔熵熵值越大说明网络结构越规则可预测性越高。在WS小世界网络和BA无标度网络上该指标与多种链路预测算法的AUC分数相关系数达0.83。进一步定义漫画效应强度系数即缺失10%边时添加预测边后瓦解效率提升的比例实验表明当缺失率在15%-25%时系数达到峰值1.35。import numpy as np import networkx as nx from scipy.linalg import eigh def spectral_predictability(G, k10): L nx.laplacian_matrix(G).toarray() eigvals, _ eigh(L) eigvals_sorted np.sort(eigvals)[1:k1] # 忽略零特征值 # 计算间隔熵 gaps np.diff(eigvals_sorted) gap_dist gaps / np.sum(gaps) entropy -np.sum(gap_dist * np.log(gap_dist1e-10)) return entropy / np.log(k) # 归一化 def comic_effect_strength(G_orig, G_missing, predictor, missing_frac0.15): # G_missing: 移除部分边后的网络 pred_edges predictor.predict(G_missing, top_nint(missing_frac*G_orig.number_of_edges())) G_recon G_missing.copy() G_recon.add_edges_from(pred_edges) # 计算瓦解效率 (例如基于节点度秩的攻击) def dismantle_efficiency(G): nodes_sorted sorted(G.degree, keylambda x: x[1], reverseTrue) removed 0 for n, deg in nodes_sorted: G.remove_node(n) removed 1 if nx.is_connected(G): continue else: break return removed / G.number_of_nodes() eff_missing dismantle_efficiency(G_missing.copy()) eff_recon dismantle_efficiency(G_recon.copy()) eff_full dismantle_efficiency(G_orig.copy()) return (eff_recon - eff_missing) / (eff_full - eff_missing)
