2 citations · 2 across the 11 of their papers we have counts for
6 papers · 1 filter
NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption
Ziluowen Luo, Jun Yin, Ruochen Liu +4
Post-hoc Graph Neural Network (GNN) explainers commonly follow a Perturb-Query paradigm, inferring the importance of graph elements based on queried predictions to perturbed inputs…
Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs
Hao Yan, Xuanru Wang, Jun Yin +3
Multimodal Attributed Graph Learning (MAGL) integrates intrinsic node attributes with structural topology via graph aggregation. However, as pretrained encoders evolve into Large F…
Rethinking Deep Research from the Perspective of Web Content Distribution Matching
Zixuan Yu, Zhenheng Tang, Tongliang Liu +3
Despite the integration of search tools, Deep Search Agents often suffer from a misalignment between reasoning-driven queries and the underlying web indexing structures. Existing f…
From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection
Yixin Liu, Shiyuan Li, Yu Zheng +4
Graph anomaly detection (GAD) is critical for identifying abnormal nodes in graph-structured data from diverse domains, including cybersecurity and social networks. The existing GA…
Estimating Time Series Foundation Model Transferability via In-Context Learning
Qingren Yao, Ming Jin, Chengqi Zhang +3
Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training, yet fine-tuning remains critical for boosting performance in domains with lim…
ARC: A Generalist Graph Anomaly Detector with In-Context Learning
Yixin Liu, Shiyuan Li, Yu Zheng +3
Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention. However, current GAD methods…