| ID | 原文 | 译文 |
| 1773 | 在网络结构不断变化的同时，社区结构也随之演化。 | The internal community structure is evolving with the change of network structure. |
| 1774 | 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。 | These changes in differ-ent time slices can be defined as four different evolutionary events:survive, split, fusion and disappearance. |
| 1775 | 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。 | In this paper, the network representation learning method is used to map the graph embedding of the network into the low-dimensional vectorspace to study the prediction of dynamic community evolution events. |
| 1776 | 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; | In the features, based on the features of community in-ternal attributes, the change of temporal attributes, and the previous community evolution events, the potential structure char-acteristics of the four evolutionary events are introduced and obtained by using random walk and Softmax. |
| 1777 | 模型方面，引入深度随机森林的策略， | In the model, thes trategy of deep random forest is proposed. |
| 1778 | 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。 | Feature fusion and feature training are carried out by using the attention mecha-nism and Monte Carlo feature sampling strategy, which overcomes the shortcomings of the existing algorithms that only ac-quire local structural features. |
| 1779 | 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。 | Finally, by comparing SVM, XGBOOST, RIDGE model training in the DBLP, FACEBOOKand 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. |
| 1780 | 针对鲸鱼优化算法(Whale Optimization Algorithm，WOA)存在的收敛速度慢、寻优稳定性不足等问题，本文提出了精英反向学习的黄金正弦鲸鱼优化算法(Elite Opposition-Based Golden-Sine Whale Optimization Algorithm，EGolden-SWOA)。 | In order to improve the slow convergence rate and low stability of WOA, elite opposition-based golden-sinewhale optimization algorithm is proposed. Elite opposition-based learning strategy is used to improve the diversity and quali-ty of the population so that the convergence rate can be promoted. |
| 1781 | 利用精英反向学习策略提高种群的多样性和质量可以有效提升算法的收敛速度，同时引入黄金分割数优化 WOA 的寻优方式，从而协调算法的全局探索与局部开发能力。 | At the same time, golden ratio is introduced to improve the optimal method of WOA, so as to coordinate the global exploration and local exploitation. |
| 1782 | 对 20 个单模态和多模态测试函数进行寻优实验，并与 RLPSO(Reverse-learning and Local-learning Particle Swarm Optimization)、IWOA(Improved Whale Optimization Al-gorithm based on nonlinear convergence factor)等多个算法进行对比， | Twenty unimodal and multi-modal benchmark functions are tested and compared with that of other algorithms, |