197 citations · 339 across the 28 of their papers we have counts for
12 papers · 1 filter
Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness-Generalization Perspective
Ming Gu, Zhuonan Zheng, Sheng Zhou +5
Graph Neural Networks (GNNs) have achieved great success but are often considered to be challenged by varying levels of homophily in graphs. Recent \textit{empirical} studies have…
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer Pruning
Qingsong Lv, Jiasheng Sun, Sheng Zhou +6
To reduce computational overhead while maintaining model performance, model pruning techniques have been proposed. Among these, structured pruning, which removes entire convolution…
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Yuanchen Bei, Weizhi Chen, Hao Chen +5
Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one. Alth…
Is Cognition Consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding
Zirui Shao, Feiyu Gao, Zhaoqing Zhu +6
Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding, a rapidly growing research area with significant industrial demand. As a mult…
WebRPG: Automatic Web Rendering Parameters Generation for Visual Presentation
Zirui Shao, Feiyu Gao, Hangdi Xing +5
In the era of content creation revolution propelled by advancements in generative models, the field of web design remains unexplored despite its critical role in modern digital com…
ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data
Yufan Shen, Chuwei Luo, Zhaoqing Zhu +5
Recently, large language models (LLMs) and multimodal large language models (MLLMs) have demonstrated promising results on document visual question answering (VQA) task, particular…
Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation
Meihan Liu, Zhen Zhang, Jiachen Tang +3
Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a label-rich source graph to an unlabeled target graph under domain discrepancies. Despite the p…
Better Late Than Never: Formulating and Benchmarking Recommendation Editing
Chengyu Lai, Sheng Zhou, Zhimeng Jiang +5
Recommendation systems play a pivotal role in suggesting items to users based on their preferences. However, in online platforms, these systems inevitably offer unsuitable recommen…
Heterophilous Distribution Propagation for Graph Neural Networks
Zhuonan Zheng, Sheng Zhou, Hongjia Xu +6
Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success r…
Revisiting the Message Passing in Heterophilous Graph Neural Networks
Zhuonan Zheng, Yuanchen Bei, Sheng Zhou +6
Graph Neural Networks (GNNs) have demonstrated strong performance in graph mining tasks due to their message-passing mechanism, which is aligned with the homophily assumption that…
Rethinking Propagation for Unsupervised Graph Domain Adaptation
Meihan Liu, Zeyu Fang, Zhen Zhang +4
Unsupervised Graph Domain Adaptation (UGDA) aims to transfer knowledge from a labelled source graph to an unlabelled target graph in order to address the distribution shifts betwee…
A Survey on Graph Condensation
Hongjia Xu, Liangliang Zhang, Yao Ma +3
Analytics on large-scale graphs have posed significant challenges to computational efficiency and resource requirements. Recently, Graph condensation (GC) has emerged as a solution…