In this project, students are encouraged to design a GNN model which can deal with heterogeneous graphs. A layer graph specifies the architecture of a deep learning network with a more complex graph structure in which layers can have inputs from multiple layers and outputs to multiple layers. Recently IBM Research and others have made big steps forward on scalability, triggering an exciting acceleration in the field. Deep learning for recommender systems In class, we have learned several deep learning models for recommender systems. Deep learning is developing as an important technology to perform various tasks in cheminformatics. Deep learning on graphs has lagged other segments of AI because the combinatorial complexity and nonlinearity of graphs requires long training times. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Recently, many novel deep learning techniques have been developed to process graph data.
More and more works have applied these graph-based deep learning techniques in various traffic tasks and have achieved state-of-the art performances. Graph learning is powerful for industry applications.
Learning Graph-Level Representations with Recurrent Neural Networks (Arxiv 2018) Yu Jin and Joseph F. JaJa [Python Reference] ... An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018) Muhan Zhang, Zhicheng Cui, Marion Neumann and Yixin Chen [Python Tensorflow Reference] [Python Pytorch Reference] [MATLAB Reference] [Python Alternative] [Python … To provide a comprehensive and clear picture of such emerging trend, this survey carefully examines various graph-based deep learning architectures in … Deep Learning on Graph-Structured Data Thomas Kipf Semi-supervised classification on graphs 15 Embedding-based approaches Two-step pipeline: 1) Get embedding for every node 2) Train classifier on node embedding Examples: DeepWalk [Perozzi et al., 2014], node2vec [Grover & Leskovec, 2016] Problem: Embeddings are not optimized for classification! Deep learning with graph features This means to tabularize the graph data, then run a traditional feed-forward network on it.
The deep learning for graphs field is rooted in neural networks for graphs research and early 1990s works on Recursive Neural Networks (RecNN) for tree structured data.
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More and more works have applied these graph-based deep learning techniques in various traffic tasks and have achieved state-of-the art performances. Graph learning is powerful for industry applications.
Learning Graph-Level Representations with Recurrent Neural Networks (Arxiv 2018) Yu Jin and Joseph F. JaJa [Python Reference] ... An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018) Muhan Zhang, Zhicheng Cui, Marion Neumann and Yixin Chen [Python Tensorflow Reference] [Python Pytorch Reference] [MATLAB Reference] [Python Alternative] [Python … To provide a comprehensive and clear picture of such emerging trend, this survey carefully examines various graph-based deep learning architectures in … Deep Learning on Graph-Structured Data Thomas Kipf Semi-supervised classification on graphs 15 Embedding-based approaches Two-step pipeline: 1) Get embedding for every node 2) Train classifier on node embedding Examples: DeepWalk [Perozzi et al., 2014], node2vec [Grover & Leskovec, 2016] Problem: Embeddings are not optimized for classification! Deep learning with graph features This means to tabularize the graph data, then run a traditional feed-forward network on it.
The deep learning for graphs field is rooted in neural networks for graphs research and early 1990s works on Recursive Neural Networks (RecNN) for tree structured data.
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