64 citations · 126 across the 31 of their papers we have counts for
6 papers · 1 filter
Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
Hanyang Yuan, Jiarong Xu, Renhong Huang +3
Graph neural networks (GNNs) have attracted considerable attention due to their diverse applications. However, the scarcity and quality limitations of graph data present challenges…
Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training
Zhou Zhang, Dongzeng Tan, Jiaan Wang +2
Emojis have gained immense popularity on social platforms, serving as a common means to supplement or replace text. However, existing data mining approaches generally either comple…
Can Modifying Data Address Graph Domain Adaptation?
Renhong Huang, Jiarong Xu, Xin Jiang +2
Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to d…
Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data
Hanyang Yuan, Jiarong Xu, Cong Wang +4
The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studi…
LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
Yiran Qiao, Xiang Ao, Yang Liu +3
Recent prevailing works on graph machine learning typically follow a similar methodology that involves designing advanced variants of graph neural networks (GNNs) to maintain the s…
CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Models
Haoxiang Shi, Jiaan Wang, Jiarong Xu +2
Text-to-Table aims to generate structured tables to convey the key information from unstructured documents. Existing text-to-table datasets are typically oriented English, limiting…