Temporal graphs serve as a powerful framework for representing networks whose connections evolve over time. By incorporating time‐stamped interactions, these models capture the dynamic nature of ...
Network or graph is a mathematical description of the internal structure between components in a complex system, such as connections between neurons, interactions between proteins, contacts between ...
Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is ...
Scholars deliver the first systematic survey of Dynamic GNNs, unifying continuous- and discrete-time models, benchmarking ...
Franz’s AllegroGraph 7.2 Powers Enterprise Data Fabrics With Graph Neural Networks, Virtual Graphs and Streaming Graph Pipelines Organizations Gain ‘Next Level AI’ by Merging Knowledge Graphs with ...
Adapting to the Stream: An Instance-Attention GNN Method for Irregular Multivariate Time Series Data
DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...
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