主题介绍
Chain-of-thought (CoT) prompting has achieved remarkable success in natural language processing (NLP). However, its vast potential remains largely unexplored for graphs. This raises an interesting question: How can we design CoT prompting for graphs to guide graph models to learn step by step? On one hand, unlike natural languages, graphs are non-linear and characterized by complex topological structures. On the other hand, many graphs lack textual data, making it difficult to formulate language-based CoT prompting. In this work, we propose the first CoT prompt learning framework for text-free graphs, GCoT. Specifically, we decompose the adaptation process for each downstream task into a series of inference steps, with each step consisting of prompt-based inference, “thought” generation, and thought-conditionedprompt learning. While the steps mimic CoT prompting in NLP, the exact mechanism differs significantly. Specifically, at each step, an input graph, along with a prompt, is first fed into a pre-trained graph encoder for prompt-based inference. We then aggregate the hidden layers of the encoder to construct a “thought”, which captures the working state of each node in the current step. Conditioned on this thought, we learn a prompt specific to each node based on the current state. These prompts are fed into the next inference step, repeating the cycle. To evaluate and analyze the effectiveness of GCoT, we conduct comprehensive experiments on eight public datasets, which demonstrate the advantage of our approach.
分享嘉宾
于星橦，现任新加坡管理大学博士后。他本科毕业于中国科学技术大学少年班学院，并在中国科学技术大学计算机科学与技术学院获得博士学位。他的研究方向包括图学习、提示学习以及图基础模型。他作为第一作者在ICLR,SIGKDD,WWW,AAAI等国际会议发表多篇论文。他的一篇工作被Paper Digest评为WWW2023最有影响力论文。此外，他还担任了多个顶级国际会议的审稿人，包括 ICLR，NeurIPS，ICML，SIGKDD，WWW，AAAI 等。
