| 康驻关,金福生,王国仁.基于Motif聚集系数与时序划分的高阶链接预测方法.软件学报,2021,32(3):3-0 |
| 基于Motif聚集系数与时序划分的高阶链接预测方法 |
| High-Order Link Prediction Method Based on Motif Aggregation Coefficient and Time Series Division |
| 投稿时间：2020-07-09 修订日期：2020-11-06 |
| DOI：10.13328/j.cnki.jos.006172 |
| 中文关键词: 动态网络 链接预测 高阶网络结构 图机器学习 |
| 英文关键词:Dynamic networks Link prediction High-order network structure Graph based machine learning |
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| 摘要点击次数: 118 |
| 全文下载次数: 60 |
| 中文摘要: |
| 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.本文提出了一种基于Motif聚集系数与时序划分的高阶链接预测模型（简称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，本文所提出的MTLP模型具有更好的高阶链接预测性能. |
| 英文摘要: |
| 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 paper 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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