no code implementations • 7 May 2024 • Zhihao Wen, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu
To capture the nuances of the temporal and spatial relationships and heterogeneous characteristics in an interconnected graph of sensors, we introduce a novel model named Temporal and Heterogeneous Graph Neural Networks (THGNN).
no code implementations • 19 Feb 2024 • Zhihao Wen, Jie Zhang, Yuan Fang
Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time.
no code implementations • 2 Feb 2024 • Xingtong Yu, Yuan Fang, Zemin Liu, Yuxia Wu, Zhihao Wen, Jianyuan Bo, Xinming Zhang, Steven C. H. Hoi
Finally, we outline prospective future directions for few-shot learning on graphs to catalyze continued innovation in this field.
no code implementations • 19 Aug 2023 • Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao
We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap.
1 code implementation • 15 Jul 2023 • Zhihao Wen, Yuan Fang
During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore handcrafted discrete prompts and continuous prompt tuning for the jointly pre-trained model to achieve zero- and few-shot classification, respectively.
no code implementations • 5 May 2023 • Zhihao Wen, Yuan Fang
Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions.
no code implementations • 14 May 2021 • Zhihao Wen, Yuan Fang, Zemin Liu
That is, MI-GNN does not directly learn an inductive model; it learns the general knowledge of how to train a model for semi-supervised node classification on new graphs.