GRAND+: Scalable Graph Random Neural Networks

1 code implementation • 12 Mar 2022 • Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang

In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.

Data Augmentation Graph Learning +2

Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks

no code implementations • 8 Mar 2022 • Jibing Gong, Yao Wan, Ye Liu, Xuewen Li, Yi Zhao, Cheng Wang, YuTing Lin, Xiaohan Fang, Wenzheng Feng, Jingyi Zhang, Jie Tang

Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise.

Graph Attention reinforcement-learning +1

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks

1 code implementation • 30 Dec 2021 • Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang

Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements.

Benchmarking

SCR: Training Graph Neural Networks with Consistency Regularization

4 code implementations • 8 Dec 2021 • Chenhui Zhang, Yufei He, Yukuo Cen, Zhenyu Hou, Wenzheng Feng, Yuxiao Dong, Xu Cheng, Hongyun Cai, Feng He, Jie Tang

However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.

Node Classification Node Property Prediction

CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations

1 code implementation • 5 Nov 2021 • Tianyu Zhang, Yuxiang Ren, Wenzheng Feng, Weitao Du, Xuecang Zhang

In this paper, we show the potential hazards of inappropriate augmentations and then propose a novel Collaborative Graph Contrastive Learning framework (CGCL).

Contrastive Learning Data Augmentation +2

Graph Random Neural Networks for Semi-Supervised Learning on Graphs

1 code implementation • NeurIPS 2020 • Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.

Data Augmentation Node Classification

MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs

no code implementations • ACL 2020 • Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang

The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e. g., course concept extraction, prerequisite relation discovery, etc.

Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

2 code implementations • 23 Jun 2020 • Shen Wang, Jibing Gong, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu

To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network.

Representation Learning

Graph Random Neural Network for Semi-Supervised Learning on Graphs

7 code implementations • 22 May 2020 • Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.

Data Augmentation Graph Learning +1

Beihang-MSRA at SemEval-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering

no code implementations • SEMEVAL 2017 • Wenzheng Feng, Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou

This paper presents the system in SemEval-2017 Task 3, Community Question Answering (CQA).

Answer Selection Community Question Answering +4