10 citations · 22 across the 7 of their papers we have counts for
7 papers
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-…
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
Zhen Tan, Song Wang, Kaize Ding +2
Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. This problem has drawn tremendous at…
Supervised Graph Contrastive Learning for Few-shot Node Classification
Zhen Tan, Kaize Ding, Ruocheng Guo +1
Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis. But given the high cost of graph anno…
Degree-Aware Alignment for Entities in Tail
Weixin Zeng, Xiang Zhao, Wei Wang +2
Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge.…