标题： 多模态深度网络嵌入整合结构和属性信息 显示英文标题

标题： Multimodal Deep Network Embedding with Integrated Structure and Attribute Information

摘要： 网络嵌入是学习网络中节点的低维表示的过程，同时保留节点特征。 现有研究仅利用网络结构信息并专注于保留结构特征。 然而，现实网络中的节点通常具有丰富的属性，提供额外的语义信息。 已经证明，结构特征和属性特征对于网络分析任务都很重要。 为了保留这两种特征，我们研究了整合结构和属性信息以进行网络嵌入的问题，并提出了一种多模态深度网络嵌入（MDNE）方法。 MDNE 使用由多个非线性函数层组成的深度模型来捕捉非线性网络结构以及结构和属性之间的复杂交互。 由于结构和属性是两种不同类型的信息，采用多模态学习方法对其进行预处理，以帮助模型更好地捕捉节点结构和属性信息之间的相关性。 我们在损失函数中同时使用结构接近性和属性接近性来保留各自的特征，表示通过最小化损失函数获得。 在四个真实数据集上的大量实验结果表明，所提出的方法在各种任务上显著优于基线方法，这证明了我们方法的有效性和通用性。 显示英文摘要

摘要： Network embedding is the process of learning low-dimensional representations for nodes in a network, while preserving node features. Existing studies only leverage network structure information and focus on preserving structural features. However, nodes in real-world networks often have a rich set of attributes providing extra semantic information. It has been demonstrated that both structural and attribute features are important for network analysis tasks. To preserve both features, we investigate the problem of integrating structure and attribute information to perform network embedding and propose a Multimodal Deep Network Embedding (MDNE) method. MDNE captures the non-linear network structures and the complex interactions among structures and attributes, using a deep model consisting of multiple layers of non-linear functions. Since structures and attributes are two different types of information, a multimodal learning method is adopted to pre-process them and help the model to better capture the correlations between node structure and attribute information. We employ both structural proximity and attribute proximity in the loss function to preserve the respective features and the representations are obtained by minimizing the loss function. Results of extensive experiments on four real-world datasets show that the proposed method performs significantly better than baselines on a variety of tasks, which demonstrate the effectiveness and generality of our method.