6 citations · 10 across the 8 of their papers we have counts for
8 papers
UPER: Efficient Utility-driven Partially-ordered Episode Rule Mining
Hong Lin, Wensheng Gan, Junyu Ren +1
Episode mining is a fundamental problem in analyzing a sequence of numerous events. For discovering strong relationships between events in a complex event sequence, episode rule mi…
Guided Exploration of Sequential Rules
Wensheng Gan, Gengsen Huang, Junyu Ren +1
In pattern mining, sequential rules provide a formal framework to capture the temporal relationships and inferential dependencies between items. However, the discovery process is c…
Event Extraction in Large Language Model
Bobo Li, Xudong Han, Jiang Liu +11
Large language models (LLMs) and multimodal LLMs are changing event extraction (EE): prompting and generation can often produce structured outputs in zero shot or few shot settings…
Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks
Tzu-Ling Lin, Wei-Chih Chen, Teng-Fang Hsiao +7
Peer review is essential for maintaining academic quality, but the increasing volume of submissions places a significant burden on reviewers. Large language models (LLMs) offer pot…
Effective Index Construction Algorithm for Optimal -cores Computation
Shengli Sun, Peng Xu, Guanming Jiang +2
Computing -cores from uncertain graphs is a fundamental problem in uncertain graph analysis. UCF-Index is the state-of-the-art resolution to support -core queries, al…
Uncertainty Quantification on Graph Learning: A Survey
Chao Chen, Chenghua Guo, Rui Xu +6
Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustworthiness are often limited by the…
Graph Neural Architecture Search with GPT-4
Haishuai Wang, Yang Gao, Xin Zheng +3
Graph Neural Architecture Search (GNAS) has shown promising results in finding the best graph neural network architecture on a given graph dataset. However, existing GNAS methods s…
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Zheng Wang, Hongming Ding, Li Pan +3
Graph-based semi-supervised learning (GSSL) has long been a hot research topic. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph…