标题： 基于时空图嵌入的动态社交网络社区发现算法 显示英文标题

标题： Community Discovery Algorithm Based on Spatio-temporal Graph Embedding in Dynamic Social Networks

摘要： 社区发现是动态社交网络研究中的关键问题之一。 传统社区发现算法仅关注节点之间连接的建立和断开，未能捕捉更深层次的因素。 为解决这一限制，本文提出了一种基于时空图嵌入的社区发现算法（CDA-SGE），该算法结合空间信息和节点演化，全面捕捉网络的动态特征。 具体而言，该算法使用图卷积神经网络（GCN）聚合潜在的空间信息，有效表示节点在空间中的嵌入。 随后，利用门控循环单元（GRU）对节点的时间演化进行建模，从而解决节点动态性和关系传递等问题。 最后，应用自组织映射（SOM）对动态网络表示进行聚类，并识别节点的社区归属。 我们对四种类型的动态网络进行了仿真，并表明CDA-SGE在纯度、标准化互信息、异质性和同质性方面优于传统社区发现算法。 这些结果展示了该算法在准确揭示动态社交网络中隐藏的社区结构方面的优越能力。 显示英文摘要

摘要： Community discovery is one of the key issues in the study of dynamic social networks. Traditional community discovery algorithms only focus on the establishment and disconnection of connections between nodes, failing to capture deeper factors. To address this limitation, in this work, we propose a community discovery algorithm based on spatiotemporal graph embedding (CDA-SGE), which integrates spatial information and evolutions of nodes to comprehensively capture the dynamic features of networks. Specifically, this algorithm employs Graph Convolutional Neural Networks (GCN) to aggregate latent spatial information, effectively representing the embedding of nodes in space. Temporal evolutions of the nodes are then modeled using Gated Recurrent Units (GRU), thereby solving problems such as node dynamism and relationship transmission. Finally, a Self-Organizing Map (SOM) is applied to cluster dynamic network representations and identify community affiliations of nodes. We then perform simulations on four types of dynamic networks and show that the CDA-SGE outperforms traditional community discovery algorithms in terms of purity, standardized mutual information, heterogeneity, and homogeneity. These results demonstrate the algorithm's superior ability to accurately uncover community structures hidden in dynamic social networks.