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Found 12 papers, 10 papers with code
Robust Angular Synchronization via Directed Graph Neural Networks
The angular synchronization problem aims to accurately estimate (up to a constant additive phase) a set of unknown angles $\theta_1, \dots, \theta_n\in[0, 2\pi)$ from $m$ noisy measurements of their offsets $\theta_i-\theta_j \;\mbox{mod} \; 2\pi.$ Applications include, for example, sensor network localization, phase retrieval, and distributed clock synchronization.
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion
Real-world data generation often involves complex inter-dependencies among instances, violating the IID-data hypothesis of standard learning paradigms and posing a challenge for uncovering the geometric structures for learning desired instance representations.
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
In these experiments, we consider tasks related to signed information, tasks related to directional information, and tasks related to both signed and directional information.
CEP3: Community Event Prediction with Neural Point Process on Graph
Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represented as an edge along with its occurrence timestamp in the graphs. However, most previous works approach the problem in compromised settings, either formulating it as a link prediction task on the graph given the event time or a time prediction problem given which event will happen next.
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
While many networks are signed or directed, or both, there is a lack of unified software packages on graph neural networks (GNNs) specially designed for signed and directed networks.
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
In this paper, we introduce neural networks into the ranking recovery problem by proposing the so-called GNNRank, a trainable GNN-based framework with digraph embedding.
SSSNET: Semi-Supervised Signed Network Clustering
Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited.
DIGRAC: Digraph Clustering Based on Flow Imbalance
DIGRAC optimizes directed flow imbalance for clustering without requiring label supervision, unlike existing graph neural network methods, and can naturally incorporate node features, unlike existing spectral methods.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing.
MagNet: A Neural Network for Directed Graphs
In this paper, we propose MagNet, a spectral GNN for directed graphs based on a complex Hermitian matrix known as the magnetic Laplacian.
Scan-flood Fill(SCAFF): an Efficient Automatic Precise Region Filling Algorithm for Complicated Regions
Experimental results show that the proposed algorithm can generate precise masks that allow for various machine learning tasks such as supervised training.
Graphics
Ro-SOS: Metric Expression Network (MEnet) for Robust Salient Object Segmentation
In this paper, we propose an end-to-end generic salient object segmentation model called Metric Expression Network (MEnet) to deal with saliency detection with the tolerance of distortion.
