most citedGraph Neural Networks for Graphs with Heterophily: A Survey

100 citations · 223 across the 6 of their papers we have counts for

collaborators

6 papers

cs.IR202616 cited

Trustworthiness in Retrieval-Augmented Generation Systems: A Survey

Yujia Zhou, Wenbo Zhang, Jingying Shao +10

Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). Although existing research mainly emphasizes accu…

cs.CL20262 cited

LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey

Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu +17

Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. However, fully autonomous LLM-based agents still face significant…

cs.CL20265 cited

Harnessing Multiple Large Language Models: A Survey on LLM Ensemble

Zhijun Chen, Xiaodong Lu, Jingzheng Li +12

LLM Ensemble -- which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from thei…

cs.LG2026100 cited

Graph Neural Networks for Graphs with Heterophily: A Survey

Xin Zheng, Yi Wang, Yixin Liu +5

Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assu…

cs.LG2026

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…

cs.SI2025

A Survey on Location-Driven Influence Maximization

Taotao Cai, Quan Z. Sheng, Xiangyu Song +5

Influence Maximization (IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, is an evergreen hot research topic. Its…