most citedRobust Mid-Pass Filtering Graph Convolutional Networks

39 citations · 77 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2023

Text-to-Image Generation for Abstract Concepts

Jiayi Liao, Xu Chen, Qiang Fu +5

Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the express…

cs.SE2023

SoTaNa: The Open-Source Software Development Assistant

Ensheng Shi, Fengji Zhang, Yanlin Wang +6

Software development plays a crucial role in driving innovation and efficiency across modern societies. To meet the demands of this dynamic field, there is a growing need for an ef…

cs.IR20231 cited

On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering

Jiayan Guo, Lun Du, Xu Chen +5

Collaborative filtering (CF) is an important research direction in recommender systems that aims to make recommendations given the information on user-item interactions. Graph CF h…

cs.SE20231 cited

Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond

Ensheng Shi, Yanlin Wang, Hongyu Zhang +4

Recently, fine-tuning pre-trained code models such as CodeBERT on downstream tasks has achieved great success in many software testing and analysis tasks. While effective and preva…

cs.SI202336 cited

Homophily-oriented Heterogeneous Graph Rewiring

Jiayan Guo, Lun Du, Wendong Bi +6

With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG) have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown great potenti…

cs.LG202339 cited

Robust Mid-Pass Filtering Graph Convolutional Networks

Jincheng Huang, Lun Du, Xu Chen +3

Graph convolutional networks (GCNs) are currently the most promising paradigm for dealing with graph-structure data, while recent studies have also shown that GCNs are vulnerable t…