activity
20242026
most citedRethinking Structure Learning For Graph Neural Networks

1 citations · 2 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2026

ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks

Ta Thanh Thuy, Jiaqi Zhu, Xuan Liu +6

Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and content moderation. Existing datasets capture…

cs.SE2025

WebDevJudge: Evaluating (M)LLMs as Critiques for Web Development Quality

Chunyang Li, Yilun Zheng, Xinting Huang +5

The paradigm of LLM-as-a-judge is emerging as a scalable and efficient alternative to human evaluation, demonstrating strong performance on well-defined tasks. However, its reliabi…

cs.LG2025

Exploring Heterophily in Graph-level Tasks

Qinhan Hou, Yilun Zheng, Xichun Zhang +2

While heterophily has been widely studied in node-level tasks, its impact on graph-level tasks remains unclear. We present the first analysis of heterophily in graph-level learning…

cs.CL2025

FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis

Yilun Zheng, Sha Li, Fangkun Wu +9

Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own, often for humor, provocation, or controversy. Detecting and an…

cs.LG20241 cited

Rethinking Structure Learning For Graph Neural Networks

Yilun Zheng, Zhuofan Zhang, Ziming Wang +4

To improve the performance of Graph Neural Networks (GNNs), Graph Structure Learning (GSL) has been extensively applied to reconstruct or refine original graph structures, effectiv…

cs.LG2024

Is Graph Convolution Always Beneficial For Every Feature?

Yilun Zheng, Xiang Li, Sitao Luan +2

Graph Neural Networks (GNNs) have demonstrated strong capabilities in processing structured data. While traditional GNNs typically treat each feature dimension equally during graph…