9 papers
Graph Navier Stokes Networks
Zexing Zhao, Guangsi Shi, Yu Gong +4
Graph Neural Networks (GNNs) have emerged as a cornerstone of deep learning, with most existing methods rooted in graph signal processing and diffusion equations to model message p…
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…
LineageFlow: Flow Matching for High-Fidelity Family-Aware Protein Sequence Generation
Langzhang Liang, Ming Yang, Yi Feng +6
Protein sequence generation for engineering requires samples that are biophysically plausible and, when targeting a family/domain, remain recognizable members while exploring withi…
CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection
Junjun Pan, Yixin Liu, Yu Zheng +3
Text-attributed graph fraud detection (TAGFD) plays a critical role in preventing fraudulent activities on online social and e-commerce platforms. However, to evade detection, frau…
ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
Hongjiang Chen, Xin Zheng, Pengfei Jiao +5
Temporal graph neural networks (TGNNs) have gained significant traction for solving real-world temporal graph tasks. However, their interpretability remains limited, as most TGNNs…
UniER: A Unified Benchmark for Item-level and Path-level Exercise Recommendation
Xinghe Cheng, Guiyong Zhuang, Yusheng Xie +5
Personalized exercise recommendation dynamically aligns pedagogical resources with individual knowledge mastery, which is crucial for satisfying students' dynamic learning needs in…