8 citations · 11 across the 8 of their papers we have counts for
9 papers
Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection
Jialun Zheng, Hanchen Yang, Jiannong Cao +3
Generalizable dynamic graph anomaly detection (DGAD) enables pretrained detectors to identify anomalies in unseen target domains without costly retraining. However, existing method…
SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting
Mingrui Zhang, Hanchen Yang, Wengen Li +4
Large vision models (LVMs) have recently proven to be surprisingly effective time series forecasters, simply by rendering temporal data as images. This success, how ever, rests on…
AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting
Xudong Jiang, Mingshan Loo, Hanchen Yang +5
Accurate long-term time series forecasting (LTSF) requires the capture of complex long-range dependencies and dynamic periodic patterns. Recent advances in frequency-domain analysi…
DiffTSP: A Conditional Discrete Diffusion Model for Knowledge Graph Triple Set Prediction
Jihong Guan, Jiaqi Wang, Wengen Li +5
Triple set prediction (TSP) aims to discover missing facts from an incomplete knowledge graph without predefined queries. Existing approaches typically construct the predicted set…
DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes
Jialun Zheng, Jie Liu, Jiannong Cao +4
Dynamic graph anomaly detection (DGAD) is essential for identifying anomalies in evolving graphs across domains such as finance, traffic, and social networks. Recently, generalist…
OKG-LLM: Aligning Ocean Knowledge Graph with Observation Data via LLMs for Global Sea Surface Temperature Prediction
Hanchen Yang, Jiaqi Wang, Jiannong Cao +7
Sea surface temperature (SST) prediction is a critical task in ocean science, supporting various applications, such as weather forecasting, fisheries management, and storm tracking…
Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network
Jialun Zheng, Divya Saxena, Jiannong Cao +2
Inductive spatial temporal prediction can generalize historical data to predict unseen data, crucial for highly dynamic scenarios (e.g., traffic systems, stock markets). However, e…
CausalFormer: An Interpretable Transformer for Temporal Causal Discovery
Lingbai Kong, Wengen Li, Hanchen Yang +3
Temporal causal discovery is a crucial task aimed at uncovering the causal relations within time series data. The latest temporal causal discovery methods usually train deep learni…
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
Hanchen Yang, Wengen Li, Shuyu Wang +4
With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including clima…