摘要:高阶链接预测是当前网络分析研究的热点和难点，一个优秀的高阶链接预测算法不仅可以挖掘出复杂网络中节点间存在的潜在联系，还有助于认识网络结构随时间演化的规律，对于探索未知的网络关系有着重要的作用.大多数传统的链接预测算法仅考虑节点间的结构相似性特征，而忽略高阶结构的特性以及网络变化的信息.提出一种基于Motif聚集系数与时序划分的高阶链接预测模型（MTLP模型），该模型通过提取网络中高阶结构的Motif聚集系数特征和网络结构演变等特征，将其构建成可表示性特征向量，并使用多层感知器网络模型进行训练完成链接预测任务.该模型能够同时结合网络中高阶结构的聚集特征与网络结构演变信息，从而改善预测效果.通过在不同的数据集上进行实验，其结果表明，所提出的MTLP模型具有更好的高阶链接预测性能.



Abstract:High-level link prediction is a hot and difficult problem in network analysis research. An excellent high-level link prediction algorithm can not only mine the potential relationship between nodes in a complex network but also help to understand the law of network structure evolves over time. Exploring unknown network relationships has important applications. Most traditional link prediction algorithms only consider the structural similarity between nodes, while ignoring the characteristics of higher-order structures and information about network changes. This study proposes a high-order link prediction model based on Motif clustering coefficients and time series partitioning (MTLP). This model constructs a representational feature vector by extracting the features of Motif clustering coefficients and network structure evolution of high-order structures in the network, and uses multilayer perceptron (MLP) network model to complete the link prediction task. By conducting experiments on different real-life data sets, the results show that the proposed MTLP model has better high-order link prediction performance than the state-of-the-art methods.



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