基于社团多特征融合嵌入表示的时序链路预测方法

时序动态网络在静态网络基础上综合了时间属性的概念，包含了网络结构的复杂性、动态性等内涵，是研究复杂网络链路预测问题的较优思维对象，因在现实世界中具有较高应用价值而备受关注。目前大部分传统方法研究对象仍局限于静态网络，存在对网络时域演化信息利用不充分、时间复杂度较高等问题。结合社会学理论，提出一种基于社团多特征融合嵌入表示的时序链路预测方法，该方法的核心思想是通过分析网络动态演化特性，在社团范围内学习节点的嵌入表示向量，融合多特征以衡量节点间连边的生成概率。利用网络集体影响力的方法对节点和连边的权值进行计算，基于集体影响的连边权值进行社团划分，将网络划分为若干个社团子图，得到基于集体影响的相似性指标。在社团范围内，利用有偏的随机游走，结合梯度优化的Skip-gram方法获取所有节点的嵌入表示向量，得到基于社团范围游走的相似性指标。融合节点的集体影响、社团范围节点的多个中心性特征和学习到的节点表示向量，得到多特征融合的相似性指标，3 种新指标都可以用于衡量节点之间形成连边的概率。对比基于移动平均、嵌入表示、图神经网络等经典时序链路预测方法，在 6 个真实数据集上的实验结果表明，所提基于社团多特征融合的方法在 AUC评价标准下取得更优的预测性能。

Temporal link prediction method based on community multi-features fusion and embedded representation

Dynamic networks integrates time attributes on the basis of static networks, and it contains multiple connotations such as the complexity and dynamics of the network structure.It is a better thinking object for studying complex network link prediction problems in the real world.Its high application value has attracted much attention in recent years.However, most of the research objects of traditional methods are still limited to static networks, and there are problems such as insufficient utilization of network time-domain evolution information and high time complexity.Combining sociological theory, a novel temporal link prediction method was proposed based on community multi-feature fusion embedding representation.The core idea of this method was to analyze the dynamic evolution characteristics of the network, learn the embedded representation vector of nodes within the community, and effectively fuse multiple features to measure the generation probability of the connection between nodes.The network was divided into several subgraphs by using community detection with collective influence weights and the Similarity index was proposed based on the collective influence.Then, the biased random walk and the Skip-gram were used to get the embedded vectors for every node and the Similarity index was proposed based on the random walk within the community.Integrating the collective influence, multiple central features of the community, and the representation vector learned within the community, the Similarity index was proposed based on the multi-features fusion.Compared with classical temporal link prediction methods, including moving average methods, embedded representation methods, and graph neural network methods, experimental results on six real data sets show that the proposed methods based on the random walk within the community and the multi-features fusion both achieve better prediction performance under the evaluation criteria of AUC.