Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised...
Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling tr...
Cheng Cheng Yang Zhiyuan Zhiyuan Liu Cunchao Cunchao Tu
heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.
heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, ...
