3 citations · 7 across the 8 of their papers we have counts for
8 papers
Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients
Shengkun Zhu, Jinshan Zeng, Zhihua Allen-Zhao +5
Federated learning of foundation models faces a fundamental resource-asymmetry challenge: the institutions holding the most valuable domain-specific data cannot host billion-parame…
How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG
Qiming Zeng, Hao Luo, Yuhao Lin +5
By retrieving contexts from knowledge graphs, graph-based retrieval-augmented generation (GraphRAG) enhances large language models (LLMs) to generate quality answers for user quest…
Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph
Yuxiang Wang, Xiao Yan, Shiyu Jin +6
Text-attributed graph (TAG) provides a text description for each graph node, and few- and zero-shot node classification on TAGs have many applications in fields such as academia an…
Towards Scalable and Deep Graph Neural Networks via Noise Masking
Yuxuan Liang, Wentao Zhang, Zeang Sheng +5
In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in many graph mining tasks. However, scaling them to large graphs is challenging due to the high comp…
Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graph
Yuxiang Wang, Xiao Yan, Shiyu Jin +6
Text-attributed graph (TAG) is an important type of graph structured data with text descriptions for each node. Few- and zero-shot node classification on TAGs have many application…
TreeCSS: An Efficient Framework for Vertical Federated Learning
Qinbo Zhang, Xiao Yan, Yukai Ding +4
Vertical federated learning (VFL) considers the case that the features of data samples are partitioned over different participants. VFL consists of two main steps, i.e., identify t…
Learning Diffusions under Uncertainty
Hao Huang, Qian Yan, Keqi Han +4
To infer a diffusion network based on observations from historical diffusion processes, existing approaches assume that observation data contain exact occurrence time of each node…
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
Yuxiang Wang, Xiao Yan, Chuang Hu +5
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learn…