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Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs
Many physical processes can be expressed through partial differential equations (PDEs).
Real-world measurements of such processes are often collected at irregularly distributed …
Real-world measurements of such processes are often collected at irregularly distributed …
TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics
Future link prediction is a fundamental challenge in various real-world dynamic systems. To
address this, numerous temporal graph neural networks (temporal GNNs) and benchmark …
address this, numerous temporal graph neural networks (temporal GNNs) and benchmark …
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains
Heterogeneous Text-Attributed Graphs (HTAGs), where different types of entities are not
only associated with texts but also connected by diverse relationships, have gained …
only associated with texts but also connected by diverse relationships, have gained …
Expressive Power of Temporal Message Passing
Graph neural networks (GNNs) have recently been adapted to temporal settings, often
employing temporal versions of the message-passing mechanism known from GNNs. We …
employing temporal versions of the message-passing mechanism known from GNNs. We …