243 citations · 260 across the 5 of their papers we have counts for
5 papers
DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise
Tai Hasegawa, Sukwon Yun, Xin Liu +2
Graph Neural Networks (GNNs) have achieved notable success in various applications over graph data. However, recent research has revealed that real-world graphs often contain noise…
Future-Proofing Class-Incremental Learning
Quentin Jodelet, Xin Liu, Yin Jun Phua +1
Exemplar-Free Class Incremental Learning is a highly challenging setting where replay memory is unavailable. Methods relying on frozen feature extractors have drawn attention recen…
CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks
Hongjun Zhu, Shun Yuan, Xin Liu +3
Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods. Among…
Class-Incremental Learning using Diffusion Model for Distillation and Replay
Quentin Jodelet, Xin Liu, Yin Jun Phua +1
Class-incremental learning aims to learn new classes in an incremental fashion without forgetting the previously learned ones. Several research works have shown how additional data…
Advanced modularity-specialized label propagation algorithm for detecting communities in networks
Xin Liu, Tsuyoshi Murata
A modularity-specialized label propagation algorithm (LPAm) for detecting network communities was recently proposed. This promising algorithm offers some desirable qualities. Howev…