10 citations · 22 across the 7 of their papers we have counts for
4 papers · 1 filter
Disinformation Detection: An Evolving Challenge in the Age of LLMs
Bohan Jiang, Zhen Tan, Ayushi Nirmal +1
The advent of generative Large Language Models (LLMs) such as ChatGPT has catalyzed transformative advancements across multiple domains. However, alongside these advancements, they…
Contrastive Meta-Learning for Few-shot Node Classification
Song Wang, Zhen Tan, Huan Liu +1
Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining ta…
Inductive Linear Probing for Few-shot Node Classification
Hirthik Mathavan, Zhen Tan, Nivedh Mudiam +1
Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. However, the existing liter…
Virtual Node Tuning for Few-shot Node Classification
Zhen Tan, Ruocheng Guo, Kaize Ding +1
Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-…