2 citations · 4 across the 8 of their papers we have counts for
9 papers
MambaTG: Candidate-Aware State Space Modeling for Future Link Prediction on Temporal Graphs
Nguyen Hong Quan, Sitao Luan, Yilun Zheng +1
Future link prediction on temporal graphs involves both repeated interactions between previously connected node pairs and first-time interactions between pairs with no prior intera…
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…
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…
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…
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…
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…
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…
What Is Missing In Homophily? Disentangling Graph Homophily For Graph Neural Networks
Yilun Zheng, Sitao Luan, Lihui Chen
Graph homophily refers to the phenomenon that connected nodes tend to share similar characteristics. Understanding this concept and its related metrics is crucial for designing eff…
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network
Yilun Zheng, Jiahao Xu, Lihui Chen
Under circumstances of heterophily, where nodes with different labels tend to be connected based on semantic meanings, Graph Neural Networks (GNNs) often exhibit suboptimal perform…