Random walk based conditional generative model for temporal networks with attributes
We propose a novel method for graph time series generation with node and edge attributes. As graph representations for complex data become increasingly popular, we encounter many time series of graphs with temporal and attribute dependencies, such as communication networks, daily bike rentals or b...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Neural Information Processing Systems Foundation
2022
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