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Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization
Furthermore, we illustrate the underlying aspects of the proposed model in effectively capturing extensive temporal dependencies in dynamic graphs.
SCGG: A Deep Structure-Conditioned Graph Generative Model
Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems.
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation
With an increase in the number of applications where data is represented as graphs, the problem of graph generation has recently become a hot topic.
Deep Graph Generators: A Survey
Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years.
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