903 citations · 1.1k across the 16 of their papers we have counts for
8 papers · 1 filter
Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series
Yunhua Pei, Zixing Song, Jin Zheng +1
Anomaly detection in multivariate time series (MTS) is hindered by dynamic inter-variable dependencies and feature entanglement under spectral noise, and in practice, is further co…
Semi-supervised Instruction Tuning for Large Language Models on Text-Attributed Graphs
Zixing Song, Irwin King
The emergent reasoning capabilities of Large Language Models (LLMs) offer a transformative paradigm for analyzing text-attributed graphs. While instruction tuning is the prevailing…
Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
Langzhang Liang, Fanchen Bu, Zixing Song +3
The message-passing paradigm of Graph Neural Networks often struggles with exchanging information across distant nodes typically due to structural bottlenecks in certain graph regi…
Spectral Feature Augmentation for Graph Contrastive Learning and Beyond
Yifei Zhang, Hao Zhu, Zixing Song +2
Although augmentations (e.g., perturbation of graph edges, image crops) boost the efficiency of Contrastive Learning (CL), feature level augmentation is another plausible, compleme…
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
Yifei Zhang, Hao Zhu, Zixing Song +2
Graph contrastive learning (GCL) improves graph representation learning, leading to SOTA on various downstream tasks. The graph augmentation step is a vital but scarcely studied st…
ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization
Langzhang Liang, Zenglin Xu, Zixing Song +3
Graph Neural Networks (GNNs) have attracted much attention due to their ability in learning representations from graph-structured data. Despite the successful applications of GNNs…
A Survey on Deep Semi-supervised Learning
Xiangli Yang, Zixing Song, Irwin King +1
Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in…
Graph-based Semi-supervised Learning: A Comprehensive Review
Zixing Song, Xiangli Yang, Zenglin Xu +1
Semi-supervised learning (SSL) has tremendous value in practice due to its ability to utilize both labeled data and unlabelled data. An important class of SSL methods is to natural…