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Found 7 papers, 1 papers with code
Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
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).
SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning
Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time.
Few-Shot Learning on Graphs: from Meta-learning to Pre-training and Prompting
Finally, we outline prospective future directions for few-shot learning on graphs to catalyze continued innovation in this field.
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks
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.
Prompt Tuning on Graph-augmented Low-resource Text Classification
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.
Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting
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.
Meta-Inductive Node Classification across Graphs
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.
