Nature

Temporal Graphs and Dynamic Networks

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 ...
EurekAlert!

Heterogeneous spatio-temporal graph contrastive learning for point-of-interest recommendation

As one of the most crucial topics in the recommendation system field, Point-of-Interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks ...
JSTOR Daily

Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction

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 ...
EurekAlert!

Predictability of temporal networks quantified by an entropy-rate-based framework

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 ...
Business Wire

Franz’s AllegroGraph 7.2 Powers Enterprise Data Fabrics With Graph Neural Networks, Virtual Graphs and Streaming Graph Pipelines

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 ...
AlphaGalileo

A survey of dynamic graph neural networks

Scholars deliver the first systematic survey of Dynamic GNNs, unifying continuous- and discrete-time models, benchmarking ...