ATTRIBUTED GRAPH EMBEDDING ALGORITHM OF MODAL FUSION

The existing attribute graph embedding algorithms cannot capture the influence of heterogeneous attribute information on topology structure flexibly and cannot deal with the heterogeneity of attribute well. A attributed graph embedding learning algorithm based on modal fusion is proposed. The algorithm could map different types of information smoothly into the same semantic space while preserving the topological structure. A modal fusion module was designed to utilize complementary information between different modes. Experimental results show that the proposed algorithm has better performance than other comparison methods on various tasks.