BrainNet

Chen, Junru; Yang, Yang; Yu, Tao; Fan, Yingying; Mo, Xiaofei; Yang, Carl

doi:10.1145/3534678.3539178

Cited by 11 publications

(1 citation statement)

References 27 publications

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“…Graph neural networks (GNNs) have been widely applied to various applications such as social computing [5,28] , anomaly detection [30,31] , and network analysis [4]. Beyond exploring various exquisite GNN structures, recent years have witnessed a new research trend on how to train a graph model for dedicated problems.…”

Section: Introductionmentioning

confidence: 99%

All in One: Multi-Task Prompting for Graph Neural Networks

Sun

Cheng

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Recently, "pre-training and fine-tuning" has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application. However, graph tasks with node level, edge level, and graph level are far diversified, making the pre-training pretext often incompatible with these multiple tasks. This gap may even cause a "negative transfer" to the specific application, leading to poor results. Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pretrained models and various graph tasks. In this paper, we propose a novel multi-task prompting method for graph models. Specifically, we first unify the format of graph prompts and language prompts with the prompt token, token structure, and inserting pattern. In this way, the prompting idea from NLP can be seamlessly introduced to the graph area. Then, to further narrow the gap between various graph tasks and state-of-the-art pre-training strategies, we further study the task space of various graph applications and reformulate downstream problems to the graph-level task. Afterward, we introduce meta-learning to efficiently learn a better initialization for the multi-task prompt of graphs so that our prompting framework can be more reliable and general for different tasks. We conduct extensive experiments, results from which demonstrate the superiority of our method.

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