The complex data structure and massive image data of social networks pose a huge challenge to the mining of associations between social information. For accurate classification of social network images, this paper proposes a social network image classification algorithm based on multimodal deep learning. Firstly, a social network association clustering model (SNACM) was established, and used to calculate trust and similarity, which represent the degree of similarity between users. Based on artificial ant colony algorithm, the SNACM was subject to weighted stacking, and the social network image association network was constructed. After that, the social network images of three modes, i.e. RGB (red-green-blue) image, grayscale image, and depth image, were fused. Finally, a three-dimensional neural network (3D NN) was constructed to extract the features of the multimodal social network image. The proposed algorithm was proved valid and accurate through experiments. The research results provide a reference for applying multimodal deep learning to classify the images in other fields.

中文翻译：

---

基于多模式深度学习的社交网络图像分类算法

社交网络的复杂数据结构和海量图像数据对挖掘社交信息之间的关联构成了巨大挑战。为了对社交网络图像进行准确分类，提出了一种基于多模式深度学习的社交网络图像分类算法。首先，建立了一个社交网络协会聚类模型（SNACM），用于计算信任度和相似度，代表用户之间的相似度。基于人工蚁群算法，对SNACM进行加权叠加，构建了社交网络图像关联网络。此后，融合了三种模式的社交网络图像，即RGB（红绿蓝）图像，灰度图像和深度图像。最后，构建了三维神经网络（3D NN）以提取多模式社交网络图像的特征。实验证明了该算法的有效性和准确性。研究结果为应用多模式深度学习对其他领域的图像进行分类提供了参考。