5 accepted papers
Temporal graph neural networks (TGNNs) have gained significant traction in solving real-world temporal graph tasks. However, their interpretability remains limited, as most TGNNs fail to identify which historical interactions most influence a given prediction. Despite promising progress on interpret…
Graph federated learning (GFL) is increasingly utilized in domains such as social network analysis and recommendation systems, where non-IID data exist extensively and necessitate a strong emphasis on personalized learning. However, existing methods focus only on the personality among different clie
Information diffusion prediction plays a crucial role in understanding the propagation of information in social networks, encompassing both macroscopic and microscopic prediction tasks. Macroscopic prediction estimates the overall impact of information diffusion, while microscopic prediction focuses…
Thanks to the development of deep Convolutional Neural Network (CNN), face verification has achieved great success rapidly. Specifically, Deep Distance Metric Learning (DDML), as an emerging area, has achieved great improvements in computer vision community. Softmax loss is widely used to supervise…
For remaining useful lifetime (RUL) prediction with multi-channel sensory data, long-term prediction has more uncertainty than short-term prediction. In this paper, the ratio of mean to variance was considered to measure the uncertainty propagation rate (UPR) of RUL prediction over time. Furthermore…