基于层次化混合特征图的链路预测方法

Research on a link-prediction method based on a hierarchical hybrid-feature graph

现实世界中的实体连同关联关系构成了一种网络关系结构即异构信息网络.利用链路预测技术可以预测出异构信息网络中存在但未被观察到,或者未来可能会出现的链路,更好地帮助用户理解网络的结构生成和演化规律.然而,目前链路预测技术缺乏对多种特征的有效融合而影响预测准确性,且难以适应异构信息网络的异构性和动态性.本文提出了一种层次化混合特征图模型(hierarchical hybrid feature graph, HHFG),充分考虑了异构信息网络的拓扑特征、语义特征和时序特征.提出了一种基于HHFG的链路预测算法,基于混合特征在HHFG上做随机游走,并采用梯度下降法学习特征权重,转移系数等参数,有效地保证了链路预测的准确性.通过实验验证了本文所提出的关键技术的可行性和有效性.

Entities in the real world are often interconnected, forming heterogeneous information networks. Linkprediction is a necessary technique for predicting the existence of unobserved or future links in heterogeneous information networks. It is useful to make users better understand the generation and evolution of networks.However, current techniques lack the effective fusion of multiple features, often leading to nonsensical results.Also, it is difficult for them to adapt to the heterogeneity and dynamics of heterogeneous-information networks.In this paper, we present a hierarchical-hybrid-feature-graph(HHFG) model by fully considering structural,semantic, and time features. Also, an HHFG based link-prediction algorithm is proposed to effectively guarantee accuracy. On one hand, it performs a random walk on HHFG based upon hybrid features. On the other hand,parameters such as feature weights and transition coefficients are learned by the gradient-descent method. The experiments demonstrate the feasibility and effectiveness of our key techniques.