In recent years, to address the issue of networked data sparsity in node classification tasks, cross-network node classification (CNNC) leverages the richer information from a source network to enhance the performance of node classification in the target network, which typically has sparser information. However, in real-world applications, labeled nodes may be collected from multiple sources with multiple modalities (e.g., text, vision, and video). Naive application of single-source and single-modal CNNC methods may result in sub-optimal solutions. To this end, in this paper, we propose a model called M²CDNE (Multi-source and Multi-modal Cross-network Deep Network Embedding) for cross-network node classification. In M²CDNE, we propose a deep multi-modal network embedding approach that combines the extracted deep multi-modal features to make the node vector representations network-invariant. In addition, we apply dynamic adversarial adaptation to assess the significance of marginal and conditional probability distributions between each source and target network to make node vector representations label-discriminative. Furthermore, we devise to classify nodes in the target network through the related source classifier and aggregate different predictions utilizing respective network weights, corresponding to the discrepancy between each source and target network. Extensive experiments performed on real-world datasets demonstrate that the proposed M²CDNE significantly outperforms the state-of-the-art approaches.

近年来，为了解决节点分类任务中网络数据稀疏的问题，跨网络节点分类（CNNC）利用源网络中更丰富的信息来增强目标网络中节点分类的性能，目标网络通常具有稀疏信息。然而，在现实世界的应用中，标记的节点可以从具有多种模式（例如文本、视觉和视频）的多个源收集。单源和单模态 CNNC 方法的简单应用可能会导致次优解决方案。为此，在本文中，我们提出了一种称为 M ² CDNE（多源多模态跨网络深度网络嵌入）的模型，用于跨网络节点分类。在M ² CDNE中，我们提出了一种深度多模态网络嵌入方法，该方法结合提取的深度多模态特征，使节点向量表示网络不变。此外，我们应用动态对抗性适应来评估每个源网络和目标网络之间的边际和条件概率分布的重要性，以使节点向量表示具有标签判别性。此外，我们设计通过相关的源分类器对目标网络中的节点进行分类，并利用各自的网络权重聚合不同的预测，对应于每个源网络和目标网络之间的差异。对现实世界数据集进行的大量实验表明，所提出的 M ² CDNE 显着优于最先进的方法。