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Found 11 papers, 5 papers with code
Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations
iGCL designs the invariant-discriminative loss (ID loss) to learn invariant and discriminative representations.
Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions
Community Detection algorithms are used to detect densely connected components in complex networks and reveal underlying relationships among components.
Curvature Graph Neural Network
And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature.
Graph Information Vanishing Phenomenon inImplicit Graph Neural Networks
A class of GNNs solves this problem by learning implicit weights to represent the importance of neighbor nodes, which we call implicit GNNs such as Graph Attention Network.
KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks.
AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting
Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation.
RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification
On the one hand, RS-MetaNet raises the level of learning from the sample to the task by organizing training in a meta way, and it learns to learn a metric space that can well classify remote sensing scenes from a series of tasks.
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting
In this study, an attention temporal graph convolutional network (A3T-GCN) traffic forecasting method was proposed to simultaneously capture global temporal dynamics and spatial correlations.
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information.
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification
The feature-learning procedure of CNN largely depends on the architecture of CNN.
Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled.
