Multi-Source and Multi-modal Deep Network Embedding for Cross-Network Node Classification

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 article, we propose a model called Multi-source and Multi-modal Cross-network Deep Network Embedding (M 2 CDNE) for cross-network node…

• NN
  National Natural Science Foundation of China

  Awards: 2020YFB1406902, 2021007, 2020B0101360001, U22A2036
• NK
  National Key Research and Development Program of China

  Awards: 2020YFB1406902, 2020B0101360001
• FR
  Fundamental Research Funds for the Central Universities

  Awards: 2020B0101360001, HIT.OCEF.2021007, 2020YFB1406902