关键词: 复杂网络, 混合链路预测, 信息熵, 主成分分析, 特征融合

Abstract:

Aiming at the problem that traditional link prediction has computational results not stable in networks with different structures， a link prediction algorithm based on information entropy improved Principal Component Analysis （PCA） model was proposed. Firstly， seven similarity indexes were determined by Random Forest （RF） as the optimal feature set. Then， seven similarity indexes were combined to propose a feature information fusion model based on information entropy improved PCA. After weighting the feature information， the model was combined with the single mechanism algorithms to verify the correctness and verification effect of the model on six real-world datasets. Finally， the feasibility and effectiveness of the link prediction algorithm based on the proposed model were verified by comparing Area Under the Curve （AUC） values with the hybrid link prediction algorithms. Experimental results show that the proposed link prediction algorithms improve the AUC value by 2.5 to 12.46 percentage points and 0.47 to 9.01 percentage points， respectively， compared with Ordered Weighted Averaging aggregation operator （OWA） and Ensemble-Model-based Link Prediction algorithm （EMLP）. It can be seen that applying the proposed algorithm to networks with different structural features can obtain more stable and accurate link prediction results.

Key words: complex network, hybrid link prediction, information entropy, Principal Component Analysis (PCA), feature fusion