Towards data augmentation in graph neural network: An overview and evaluation
M Adjeisah, X Zhu, H Xu, TA Ayall - Computer Science Review, 2023 - Elsevier
Abstract Many studies on Graph Data Augmentation (GDA) approaches have emerged. The
techniques have rapidly improved performance for various graph neural network (GNN) …
techniques have rapidly improved performance for various graph neural network (GNN) …
EGNN: Graph structure learning based on evolutionary computation helps more in graph neural networks
Z Liu, D Yang, Y Wang, M Lu, R Li - Applied Soft Computing, 2023 - Elsevier
In recent years, graph neural networks (GNNs) have been successfully applied in many
fields due to their characteristics of neighborhood aggregation and have achieved state-of …
fields due to their characteristics of neighborhood aggregation and have achieved state-of …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Learning latent relations for temporal knowledge graph reasoning
Abstract Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on
historical data. However, due to the limitations in construction tools and data sources, many …
historical data. However, due to the limitations in construction tools and data sources, many …
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 …
violating the IID-data hypothesis of standard learning paradigms and posing a challenge for …
Smg: A micro-gesture dataset towards spontaneous body gestures for emotional stress state analysis
We explore using body gestures for hidden emotional state analysis. As an important non-
verbal communicative fashion, human body gestures are capable of conveying emotional …
verbal communicative fashion, human body gestures are capable of conveying emotional …
Self-supervised graph structure refinement for graph neural networks
Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural
networks (GNNs), has shown great potential in boosting the performance of GNNs. Most …
networks (GNNs), has shown great potential in boosting the performance of GNNs. Most …
Adversarial contrastive learning for evidence-aware fake news detection with graph neural networks
The prevalence and perniciousness of fake news have been a critical issue on the Internet,
which stimulates the development of automatic fake news detection in turn. In this paper, we …
which stimulates the development of automatic fake news detection in turn. In this paper, we …
Interpolating graph pair to regularize graph classification
We present a simple and yet effective interpolation-based regularization technique, aiming
to improve the generalization of Graph Neural Networks (GNNs) on supervised graph …
to improve the generalization of Graph Neural Networks (GNNs) on supervised graph …
Graphglow: Universal and generalizable structure learning for graph neural networks
Graph structure learning is a well-established problem that aims at optimizing graph
structures adaptive to specific graph datasets to help message passing neural networks (ie …
structures adaptive to specific graph datasets to help message passing neural networks (ie …