activity
20242026
most citedA Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions

6 citations · 6 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

RDL-Bench: A Benchmark for Task-Aware Question Answering over Relational Databases

Yao Cheng, Siqiang Luo

Relational databases (RDBs) remain the primary infrastructure for storing and managing structured data across enterprise and scientific domains. Natural-language (NL) interfaces to…

cs.AI2025

SEQ-GPT: LLM-assisted Spatial Query via Example

Ivan Khai Ze Lim, Ningyi Liao, Yiming Yang +2

Contemporary spatial services such as online maps predominantly rely on user queries for location searches. However, the user experience is limited when performing complex tasks, s…

cs.DC2025

RAGDoll: Efficient Offloading-based Online RAG System on a Single GPU

Weiping Yu, Ningyi Liao, Siqiang Luo +1

Retrieval-Augmented Generation (RAG) enhances large language model (LLM) generation quality by incorporating relevant external knowledge. However, deploying RAG on consumer-grade p…

cs.LG2024

Unifews: You Need Fewer Operations for Efficient Graph Neural Networks

Ningyi Liao, Zihao Yu, Ruixiao Zeng +1

Graph Neural Networks (GNNs) have shown promising performance, but at the cost of resource-intensive operations on graph-scale matrices. To reduce computational overhead, previous…

cs.SI2024★ 6 cited

A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions

Chenghua Gong, Yao Cheng, Jianxiang Yu +4

Graphs are structured data that models complex relations between real-world entities. Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar fe…