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Found 12 papers, 7 papers with code
SocialCVAE: Predicting Pedestrian Trajectory via Interaction Conditioned Latents
Pedestrian trajectory prediction is the key technology in many applications for providing insights into human behavior and anticipating human future motions.
Learning Scalable Structural Representations for Link Prediction with Bloom Signatures
We further show that any type of neighborhood overlap-based heuristic can be estimated by a neural network that takes Bloom signatures as input.
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
Focusing on per-instance differential privacy (pDP), our framework elucidates the potential privacy leakage for each data point in a given training dataset, offering insights into data preprocessing to reduce privacy risks of the synthetic dataset generation via DDMs.
OCTAL: Graph Representation Learning for LTL Model Checking
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification.
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning
Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and generalization ability.
Ranked #1 on
Link Property Prediction
on ogbl-ppa
OCTAL: Graph Representation Learning for LTL Model Checking
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification.
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs due to their impressive capability of capturing nonlinear relations in graph-structured data.
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks
Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif prediction and so on.
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and has demonstrated advantages in many important data science applications such as link, relation and motif prediction.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Revisiting graph neural networks and distance encoding from a practical view
We want to explain how DE makes GNNs fit for node classification and link prediction.
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling
In this U-shaped network, a paired sampling operation is proposed in spacetime domain accordingly: the pooling (ST-Pool) coarsens the input graph in spatial from its deterministic partition while abstracts multi-resolution temporal dependencies through dilated recurrent skip connections; based on previous settings in the downsampling, the unpooling (ST-Unpool) restores the original structure of spatio-temporal graphs and resumes regular intervals within graph sequences.
Ranked #1 on
Traffic Prediction
on PeMS-M
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Timely accurate traffic forecast is crucial for urban traffic control and guidance.
Ranked #2 on
Time Series Forecasting
on PeMSD7
