6 citations · 8 across the 8 of their papers we have counts for
8 papers
A Cross-graph Tuning-free GNN Prompting Framework
Yaqi Chen, Shixun Huang, Ryan Twemlow +6
GNN prompting aims to adapt models across tasks and graphs without requiring extensive retraining. However, most existing graph prompt methods still require task-specific parameter…
BIOME-Bench: A Benchmark for Biomolecular Interaction Inference and Multi-Omics Pathway Mechanism Elucidation from Scientific Literature
Sibo Wei, Peng Chen, Lifeng Dong +7
Multi-omics studies often rely on pathway enrichment to interpret heterogeneous molecular changes, but pathway enrichment (PE)-based workflows inherit structural limitations of pat…
A Community-Enhanced Graph Representation Model for Link Prediction
Lei Wang, Darong Lai
Although Graph Neural Networks (GNNs) have become the dominant approach for graph representation learning, their performance on link prediction tasks does not always surpass that o…
Head-Tail Cooperative Learning Network for Unbiased Scene Graph Generation
Lei Wang, Zejian Yuan, Yao Lu +1
Scene Graph Generation (SGG) as a critical task in image understanding, facing the challenge of head-biased prediction caused by the long-tail distribution of predicates. However,…
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models
Jiabang He, Liu Jia, Lei Wang +2
Knowledge Graph Completion (KGC) aims to conduct reasoning on the facts within knowledge graphs and automatically infer missing links. Existing methods can mainly be categorized in…
Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs
Taiqiang Wu, Zhe Zhao, Jiahao Wang +4
Distilling high-accuracy Graph Neural Networks (GNNs) to low-latency multilayer perceptions (MLPs) on graph tasks has become a hot research topic. However, conventional MLP learnin…
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification
Xiaohe Li, Lijie Wen, Yawen Deng +4
Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification. In this work, we reveal the vulnerability of GNN to the imbalance of…
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Langzhang Liang, Cuiyun Gao, Shiyi Chen +5
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, l…