73 citations · 115 across the 19 of their papers we have counts for
6 papers · 1 filter
ParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
Chaohao Yuan, Zhenjie Song, Ercan Engin Kuruoglu +5
Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the ove…
From Post To Personality: Harnessing LLMs for MBTI Prediction in Social Media
Tian Ma, Kaiyu Feng, Yu Rong +1
Personality prediction from social media posts is a critical task that implies diverse applications in psychology and sociology. The Myers Briggs Type Indicator (MBTI), a popular p…
PLACE: Prompt Learning for Attributed Community Search in Large Graphs
Shuheng Fang, Kangfei Zhao, Rener Zhang +2
In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enlightened by prompt-tuning in Natural La…
Can Large Language Models Be Query Optimizer for Relational Databases?
Jie Tan, Kangfei Zhao, Rui Li +6
Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in dat…
A Survey of Graph Transformers: Architectures, Theories and Applications
Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu +6
Graph Transformers (GTs) have demonstrated a strong capability in modeling graph structures by addressing the intrinsic limitations of graph neural networks (GNNs), such as over-sm…
Natural Language-Assisted Multi-modal Medication Recommendation
Jie Tan, Yu Rong, Kangfei Zhao +5
Combinatorial medication recommendation(CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patie…