2 citations · 2 across the 11 of their papers we have counts for
11 papers
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…
Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders
Jun Yin, Bangguo Zhu, Peng Huo +5
Recently, Generative Recommenders (GRs), characterized by a unified end-to-end framework, have exhibited astonishing potential in transforming the recommendation paradigm. Despite…
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…
Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases
Jun Yin, Peng Huo, Bangguo Zhu +4
In recent advances, to enable a fully data-driven learning paradigm on relational databases (RDB), relational deep learning (RDL) is proposed to structure the RDB as a heterogeneou…
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…
From Token Generation to Item Ranking: Direct Generative Recommendation with Semantic IDs
Yuanbo Zhao, Ruochen Liu, Senzhang Wang +6
Generative recommendation formulates item recommendation as a token-level generation task, where Semantic IDs (SIDs) represents each item as a sequence of discrete tokens. However,…
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…
Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation
Shiyuan Li, Yixin Liu, Qingsong Wen +2
Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains. The effectiveness of M…
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…
Algorithmic Fairness: A Tolerance Perspective
Renqiang Luo, Tao Tang, Feng Xia +5
Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negativ…