基于Attention深度随机森林的社区演化事件预测

在网络结构不断变化的同时，社区结构也随之演化.社区结构在不同时间片的变化可定义为四种不同的演化事件：持续、分离、融合和消失.本文运用网络表示学习的方法，对网络进行图嵌入编码映射到低维向量空间中，研究动态社区演化事件的预测.特征方面，在传统的社区内部属性特征、时间片间属性特性变化和前段时间片的社区演化事件的特征维度的基础上，引入潜在结构特征表征四种演化事件，运用随机游走和Softmax思想获取潜在的结构特征；模型方面，引入深度随机森林的策略，同时采用attention机制、蒙特卡洛特征采样策略进行特征融合和特征训练，克服了已有算法仅获取局部结构特征的缺陷.实验在DBLP、FACEBOOK和Bitcoin数据集上，对比SVM、XGBOOST和RIDGE模型训练，证实了新提出的算法模型对最终预测准确率有很大的提升.

The Community Evolution Event Prediction Based on Attention Deep Random Forest

The internal community structure is evolving with the change of network structure.These changes in different time slices can be defined as four different evolutionary events:survive,split,fusion and disappearance.In this paper,the network representation learning method is used to map the graph embedding of the network into the low-dimensional vector space to study the prediction of dynamic community evolution events.In the features,based on the features of community internal attributes,the change of temporal attributes,and the previous community evolution events,the potential structure characteristics of the four evolutionary events are introduced and obtained by using random walk and Softmax.In the model,the strategy of deep random forest is proposed.Feature fusion and feature training are carried out by using the attention mechanism and Monte Carlo feature sampling strategy,which overcomes the shortcomings of the existing algorithms that only acquire local structural features.Finally,by comparing SVM,XGBOOST,RIDGE model training in the DBLP,FACEBOOK and Bitcoin datasets,it is confirmed that the embedding feature of community structure and the attention deep random forest model improvement have greatly improved the accuracy of final prediction.