10 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…
negMIX: Negative Mixup for OOD Generalization in Open-Set Node Classification
Junwei Gong, Xiao Shen, Zhihao Chen +3
Open-set node classification (OSNC) allows unlabeled test data to contain novel classes previously unseen in the labeled data. The goal is to classify in-distribution (ID) nodes in…
GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems
Hongjiang Chen, Xin Zheng, Yixin Liu +7
Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily on the under…
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…
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
Langzhang Liang, Sunwoo Kim, Kijung Shin +3
Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graph-structured data. To empower GNNs in hete…