Despite the prevalence of recent success in learning from static graphs, learning from time-
evolving graphs remains an open challenge. In this work, we design new, more stringent …

Benefiting from the intrinsic supervision information exploitation capability, contrastive
learning has achieved promising performance in the field of deep graph clustering recently …

Abstract Many studies on Graph Data Augmentation (GDA) approaches have emerged. The
techniques have rapidly improved performance for various graph neural network (GNN) …

Graph neural networks (GNNs) are widely used for modelling graph-structured data in
numerous applications. However, with their inherently finite aggregation layers, existing …

Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto
methods to extract spatial-temporal information for temporal graph learning. Interestingly, we …

Deep learning techniques have shown their superior performance in dermatologist clinical
inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty …

Graph contrastive learning is an important method for deep graph clustering. The existing
methods first generate the graph views with stochastic augmentations and then train the …

Graph structures have attracted much research attention for carrying complex relational
information. Based on graphs, many algorithms and tools are proposed and developed for …

This work aims to estimate 6Dof (6D) object pose in background clutter. Considering the
strong occlusion and background noise, we propose to utilize the spatial structure for better …

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised
node classification in recent years. However, the problem of insufficient supervision …